Professor Frans Coenen
Professor of Computer Science Computer Science
- Work email Coenen@liverpool.ac.uk
- Personal Websitehttp://www.csc.liv.ac.uk/~frans/
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2024
Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes. (Journal article)
Irlik, K., Aldosari, H., Hendel, M., Kwiendacz, H., Piaśnik, J., Kulpa, J., . . . Nabrdalik, K. (2024). Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.. Diabetes, obesity & metabolism. doi:10.1111/dom.15578Editorial for Special Issue on “Expert decision making for data analytics with applications” (Journal article)
Yuen, K. K. F., Leu, J. -S., Ishizaka, A., Tawfik, H., & Coenen, F. (2024). Editorial for Special Issue on “Expert decision making for data analytics with applications”. Applied Soft Computing, 155, 111480. doi:10.1016/j.asoc.2024.111480Springback Prediction Using Gated Recurrent Unit and Data Augmentation (Conference Paper)
Chen, D., Coenen, F., Hai, Y., Oscoz, M. P., & Nguyen, A. (2024). Springback Prediction Using Gated Recurrent Unit and Data Augmentation. In Unknown Conference (pp. 1-13). Springer Nature Singapore. doi:10.1007/978-981-99-8498-5_12023
Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes (Preprint)
Deep ensemble learning for high-dimensional subsurface fluid flow modeling (Journal article)
Choubineh, A., Chen, J., Wood, D. A., Coenen, F., & Ma, F. (2023). Deep ensemble learning for high-dimensional subsurface fluid flow modeling. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 126. doi:10.1016/j.engappai.2023.106968An interpretable framework for sleep posture change detection and postural inactivity segmentation using wrist kinematics. (Journal article)
Elnaggar, O., Arelhi, R., Coenen, F., Hopkinson, A., Mason, L., & Paoletti, P. (2023). An interpretable framework for sleep posture change detection and postural inactivity segmentation using wrist kinematics.. Scientific reports, 13(1), 18027. doi:10.1038/s41598-023-44567-9Social Media Sentiment Analysis and Opinion Mining in Public Security: Taxonomy, Trend Analysis, Issues and Future Directions (Journal article)
Hijazi, A., & Coenen, F. (2023). Social Media Sentiment Analysis and Opinion Mining in Public Security: Taxonomy, Trend Analysis, Issues and Future Directions. Journal of King Saud University: Computer and Information Sciences.RgnTX: colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity (Journal article)
Coenen, F., Wang, Y., Meng, J., Wei, Z., & Su, J. (2023). RgnTX: colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity. Computational and Structural Biotechnology Journal. doi:10.1016/j.csbj.2023.08.021Pathology Data Prioritisation: A Study of Using Multi-variate Time Series (Conference Paper)
Qi, J., Burnside, G., & Coenen, F. (2023). Pathology Data Prioritisation: A Study of Using Multi-variate Time Series. In Unknown Conference (pp. 1-20). Springer International Publishing. doi:10.1007/978-3-031-35924-8_1Sleep posture one-shot learning framework based on extremity joint kinematics: In-silico and in-vivo case studies (Journal article)
Elnaggar, O., Coenen, F., Hopkinson, A., Mason, L., & Paoletti, P. (2023). Sleep posture one-shot learning framework based on extremity joint kinematics: In-silico and in-vivo case studies. INFORMATION FUSION, 95, 215-236. doi:10.1016/j.inffus.2023.02.003A Robust Framework of Chromosome Straightening With Vit-Patch Gan (Conference Paper)
Song, S., Wang, J., Cheng, F., Cao, Q., Zuo, Y., Lei, Y., . . . Su, J. (2023). A Robust Framework of Chromosome Straightening With Vit-Patch Gan. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE. doi:10.1109/isbi53787.2023.10230388Triple-kernel Gated Attention-based Multiple Instance Learning with Contrastive Learning for Medical Image Analysis (Journal article)
Coenen, F., Ye, R., Hu, H., Thiyagalingam, J., & Su, J. (2023). Triple-kernel Gated Attention-based Multiple Instance Learning with Contrastive Learning for Medical Image Analysis. Applied Intelligence.Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data (Journal article)
Choubineh, A., Chen, J., Coenen, F., & Ma, F. (2023). Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data. ELECTRONICS, 12(6). doi:10.3390/electronics12061453A Quantitative Insight Into the Role of Skip Connections in Deep Neural Networks of Low Complexity: A Case Study Directed at Fluid Flow Modeling (Journal article)
Choubineh, A., Chen, J., Coenen, F., & Ma, F. (2023). A Quantitative Insight Into the Role of Skip Connections in Deep Neural Networks of Low Complexity: A Case Study Directed at Fluid Flow Modeling. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 23(1). doi:10.1115/1.4054868Electrocardiogram Two-Dimensional Motifs: A Study Directed at Cardio Vascular Disease Classification (Chapter)
Aldosari, H., Coenen, F., Lip, G. Y. H., & Zheng, Y. (2023). Electrocardiogram Two-Dimensional Motifs: A Study Directed at Cardio Vascular Disease Classification. In Communications in Computer and Information Science (pp. 3-27). Springer Nature Switzerland. doi:10.1007/978-3-031-43471-6_1Forecasting the UN Sustainable Development Goals (Chapter)
Alharbi, Y., Arribas-Bel, D., & Coenen, F. (2023). Forecasting the UN Sustainable Development Goals. In Communications in Computer and Information Science (pp. 88-110). Springer Nature Switzerland. doi:10.1007/978-3-031-37320-6_5Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset (Journal article)
Choubineh, A., Chen, J., Wood, D. A., Coenen, F., & Ma, F. (2023). Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset. ALGORITHMS, 16(1). doi:10.3390/a16010024PPNNBP: A Third Party Privacy-Preserving Neural Network With Back-Propagation Learning (Journal article)
Almutairi, N., Coenen, F., & Dures, K. (2023). PPNNBP: A Third Party Privacy-Preserving Neural Network With Back-Propagation Learning. IEEE ACCESS, 11, 31657-31675. doi:10.1109/ACCESS.2023.3263114Preface (Book)
Coenen, F., Aveiro, D., Bernardino, J., Filipe, J., Fred, A., Dietz, J., & Masciari, E. (2023). Preface (Vol. 1842 CCIS).2022
Query Resolution of Literature Knowledge Graphs using Hybrid Document Embeddings. (Conference Paper)
Coenen, F., Muhammad, I., Gamble, C., Kearney, A., & Williams, P. (2022). Query Resolution of Literature Knowledge Graphs using Hybrid Document Embeddings..Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor (Conference Paper)
Coenen, F., Aldosari, H., Lip, G., & Zheng, Y. (2022). Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor.From Deterministic to Stochastic: An Interpretable Stochastic Model-free Reinforcement Learning Framework for Portfolio Optimization (Journal article)
Coenen, F., Wang, Y., Song, Z., Qian, P., Song, S., Jiang, Z., & Su, J. (2022). From Deterministic to Stochastic: An Interpretable Stochastic Model-free Reinforcement Learning Framework for Portfolio Optimization. Applied Intelligence.Retrieval-Based Language Model Adaptation for Handwritten Chinese Text Recognition (Journal article)
Coenen, F., Hu, S. -Y., Wang, Q. -F., Huang, K., & Wen, M. (2022). Retrieval-Based Language Model Adaptation for Handwritten Chinese Text Recognition. International Journal on Document Analysis and Recognition (IJDAR). doi:10.1007/s10032-022-00419-2Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation (Journal article)
Huang, D., Chen, K., Song, B., Wei, Z., Su, J., Coenen, F., . . . Meng, J. (2022). Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. NUCLEIC ACIDS RESEARCH, 50(18), 10290-10310. doi:10.1093/nar/gkac830An innovative application of deep learning in multiscale modeling of subsurface fluid flow: Reconstructing the basis functions of the mixed GMsFEM (Journal article)
Choubineh, A., Chen, J., Coenen, F., & Ma, F. (2022). An innovative application of deep learning in multiscale modeling of subsurface fluid flow: Reconstructing the basis functions of the mixed GMsFEM. Journal of Petroleum Science and Engineering, 216, 110751. doi:10.1016/j.petrol.2022.110751Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data (Journal article)
Chen, Q., Wang, W., Huang, K., & Coenen, F. (2022). Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data. IEEE INTERNET OF THINGS JOURNAL, 9(12), 9205-9213. doi:10.1109/JIOT.2021.3093065BILATERAL-VIT FOR ROBUST FOVEA LOCALIZATION (Conference Paper)
Song, S., Dang, K., Yu, Q., Wang, Z., Coenen, F., Su, J., & Ding, X. (2022). BILATERAL-VIT FOR ROBUST FOVEA LOCALIZATION. In 2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022). doi:10.1109/ISBI52829.2022.9761523Chapter Three Machine learning to improve natural gas reservoir simulations (Chapter)
Choubineh, A., Chen, J., Coenen, F., Ma, F., & Wood, D. A. (2022). Chapter Three Machine learning to improve natural gas reservoir simulations. In Sustainable Natural Gas Reservoir and Production Engineering (pp. 55-82). Elsevier. doi:10.1016/b978-0-12-824495-1.00011-5Cross-Datasets Evaluation of Machine Learning Models for Intrusion Detection Systems (Conference Paper)
Al-Riyami, S., Lisitsa, A., & Coenen, F. (2022). Cross-Datasets Evaluation of Machine Learning Models for Intrusion Detection Systems. In Unknown Conference (pp. 815-828). Springer Singapore. doi:10.1007/978-981-16-2102-4_73Data Augmentation for Pathology Prioritisation: An Improved LSTM-Based Approach (Conference Paper)
Qi, J., Burnside, G., & Coenen, F. (2022). Data Augmentation for Pathology Prioritisation: An Improved LSTM-Based Approach. In ARTIFICIAL INTELLIGENCE XXXIX, AI 2022 Vol. 13652 (pp. 51-63). doi:10.1007/978-3-031-21441-7_4FOREWORD (Conference Paper)
Coenen, F., Fred, A., & Filipe, J. (2022). FOREWORD. In International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings Vol. 1 (pp. IX-X).Pathology Data Prioritisation: A Study Using Multi-variate Time Series (Conference Paper)
Qi, J., Burnside, G., & Coenen, F. (2022). Pathology Data Prioritisation: A Study Using Multi-variate Time Series. In BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY, DAWAK 2022 Vol. 13428 (pp. 149-162). doi:10.1007/978-3-031-12670-3_13Two-dimensional Motif Extraction from Images: A Study using an Electrocardiogram (Conference Paper)
Aldosari, H., Coenen, F., Lip, G., & Zheng, Y. (2022). Two-dimensional Motif Extraction from Images: A Study using an Electrocardiogram. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications. doi:10.5220/00113805000033352021
Finding banded patternsin large data set using segmentation (Journal article)
Abdullahi, F. B., & Coenen, F. (n.d.). Finding banded patternsin large data set using segmentation. Bayero Journal of Pure and Applied Sciences, 13(1), 90-96. doi:10.4314/bajopas.v13i1.13Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs (Conference Paper)
Mansfield, M., Tamma, V., Goddard, P., & Coenen, F. (2021). Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs. In PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP '21) (pp. 129-136). doi:10.1145/3460210.3493569A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image (Conference Paper)
Song, S., Huang, D., Hu, Y., Yang, C., Meng, J., Ma, F., . . . Su, J. (2021). A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image. In 2021 14TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2021). doi:10.1109/CISP-BMEI53629.2021.9624383A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction (Journal article)
Tan, C. B., Hijazi, M. H. A., Khamis, N., Nohuddin, P. N. E. B., Zainol, Z., Coenen, F., & Gani, A. (n.d.). A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction. Multimedia Tools and Applications. doi:10.1007/s11042-021-11235-xMulti-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities (Journal article)
Coenen, F., Wang, W., Chen, Q., Huang, K., & De, S. (2021). Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities. Expert Systems With Applications. doi:10.1016/j.eswa.2021.114939Sustainable Development Goal Relational Modelling and Prediction (Journal article)
Alharbi, Y., Arribas-Bel, D., & Coenen, F. (n.d.). Sustainable Development Goal Relational Modelling and Prediction. Journal of Data Intelligence, 2(3), 348-367. doi:10.26421/jdi2.3-3Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data (Conference Paper)
Huang, D., Song, B., Wei, J., Su, J., Coenen, F., & Meng, J. (2021). Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. In BIOINFORMATICS Vol. 37 (pp. I222-I230). doi:10.1093/bioinformatics/btab278MetaTX: deciphering the distribution of mRNA-related features in the presence of isoform ambiguity, with applications in epitranscriptome analysis (Journal article)
Wang, Y., Chen, K., Wei, Z., Coenen, F., Su, J., & Meng, J. (2021). MetaTX: deciphering the distribution of mRNA-related features in the presence of isoform ambiguity, with applications in epitranscriptome analysis. BIOINFORMATICS, 37(9), 1285-1291. doi:10.1093/bioinformatics/btaa938Automated Social Text Annotation With Joint Multilabel Attention Networks (Journal article)
Dong, H., Wang, W., Huang, K., & Coenen, F. (2021). Automated Social Text Annotation With Joint Multilabel Attention Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 32(5), 2224-2238. doi:10.1109/TNNLS.2020.3002798Efficient Distributed MST Based Clustering for Recommender System (Conference Paper)
Coenen, F., & Shahzad, A. (2021). Efficient Distributed MST Based Clustering for Recommender System. In IEEE International Conference on Data Minimg (ICDM) Workshop on Advanced Neural Algorithms and Theories for Recommender Systems (NeuRec).Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification (Conference Paper)
Aldosari, H., Coenen, F., Lip, G. Y. H., & Zheng, Y. (2021). Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification. In ARTIFICIAL INTELLIGENCE XXXVIII Vol. 13101 (pp. 254-266). doi:10.1007/978-3-030-91100-3_21Document Ranking for Curated Document Databases Using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank (Conference Paper)
Muhammad, I., Bollegala, D., Coenen, F., Gamble, C., Kearney, A., & Williamson, P. (2021). Document Ranking for Curated Document Databases Using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank. In BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY (DAWAK 2021) Vol. 12925 (pp. 116-127). doi:10.1007/978-3-030-86534-4_10Event-based Pathology Data Prioritisation: A Study using Multi-variate Time Series Classification (Conference Paper)
Qi, J., Burnside, G., Charnley, P., & Coenen, F. (2021). Event-based Pathology Data Prioritisation: A Study using Multi-variate Time Series Classification. In PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT (KDIR), VOL 1: (pp. 121-128). doi:10.5220/0010643900003064Machine learning to improve natural gas reservoir simulations (Chapter)
Choubineh, A., Chen, J., Coenen, F., Ma, F., & Wood, D. A. (2022). Machine learning to improve natural gas reservoir simulations. In Sustainable Natural Gas Reservoir and Production Engineering (pp. 55-82). Elsevier. doi:10.1016/b978-0-12-824495-1.00011-5Management of non-urgent paediatric emergency department attendances by GPs: a retrospective observational study (Journal article)
Leigh, S., Mehta, B., Dummer, L., Aird, H., McSorley, S., Oseyenum, V., . . . Carrol, E. D. (2021). Management of non-urgent paediatric emergency department attendances by GPs: a retrospective observational study. BRITISH JOURNAL OF GENERAL PRACTICE, 71(702), E22-E30. doi:10.3399/bjgp20X713885Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification (Conference Paper)
Aldosari, H., Coenen, F., Lip, G. Y. H., & Zheng, Y. (2021). Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification. In BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY (DAWAK 2021) Vol. 12925 (pp. 235-241). doi:10.1007/978-3-030-86534-4_22Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping (Conference Paper)
Alshehri, M., Coenen, F., & Dures, K. (2021). Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping. In PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA) (pp. 184-191). doi:10.5220/0010519301840191Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping (Conference Paper)
Alshehri, M., Coenen, F., & Dures, K. (2021). Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping. In Proceedings of the 10th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications. doi:10.5220/0010519300002993Ranking Pathology Data in the Absence of a Ground Truth (Conference Paper)
Qi, J., Burnside, G., & Coenen, F. (2021). Ranking Pathology Data in the Absence of a Ground Truth. In ARTIFICIAL INTELLIGENCE XXXVIII Vol. 13101 (pp. 209-223). doi:10.1007/978-3-030-91100-3_18Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity (Conference Paper)
Vincent-Paulraj, A., Burnside, G., Coenen, F., Pirmohamed, M., & Walker, L. (2021). Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity. In ARTIFICIAL INTELLIGENCE XXXVIII Vol. 13101 (pp. 241-253). doi:10.1007/978-3-030-91100-3_20Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis (Conference Paper)
Alharbi, Y., Arribas-Bel, D., & Coenen, F. (2021). Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications. SCITEPRESS - Science and Technology Publications. doi:10.5220/0010546100002996Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis (Conference Paper)
Alharbi, Y., Arribas-Bel, D., & Coenen, F. (2021). Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis. In PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS (DELTA) (pp. 123-131). doi:10.5220/00105461012301312020
Candidates Reduction and Enhanced Sub-Sequence-Based Dynamic Time Warping: A Hybrid Approach (Conference Paper)
Alshehri, M., Coenen, F., & Dures, K. (2020). Candidates Reduction and Enhanced Sub-Sequence-Based Dynamic Time Warping: A Hybrid Approach. In Unknown Conference (pp. 273-285). Springer International Publishing. doi:10.1007/978-3-030-63799-6_21Adversarial Domain Adaptation for Crisis Data Classification on Social Media (Conference Paper)
Chen, Q., Wang, W., Huang, K., De, S., & Coenen, F. (2020). Adversarial Domain Adaptation for Crisis Data Classification on Social Media. In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). IEEE. doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00061DOI: 10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00061
Census Estimation using Histogram Representation of 3D Surfaces: A Case Study Focusing on the Karak Region (Journal article)
El-Salhi, S., Al-Haj, S., & Coenen, F. (n.d.). Census Estimation using Histogram Representation of 3D Surfaces: A Case Study Focusing on the Karak Region. International Journal of Advanced Computer Science and Applications, 11(10). doi:10.14569/ijacsa.2020.0111087Sustainable Development Goal Relational Modelling: Introducing the SDG-CAP Methodology (Conference Paper)
Coenen, F., Alharbi, Y., & Arribas-Bel, D. (2020). Sustainable Development Goal Relational Modelling: Introducing the SDG-CAP Methodology. In Lecture Notes in Computer Science Vol. 12393 (pp. 183-196). Bratislava, Slovakia: Springer Nature.Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media (Conference Paper)
Chen, Q., Wang, W., Huang, K., De, S., & Coenen, F. (2020). Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media. In 2020 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP) (pp. 232-237). doi:10.1109/SMARTCOMP50058.2020.00051What matters when managing childhood fever in the emergency department? A discrete-choice experiment comparing the preferences of parents and healthcare professionals in the UK (Journal article)
Leigh, S., Robinson, J., Yeung, S., Coenen, F., Carrol, E. D., & Niessen, L. W. (2020). What matters when managing childhood fever in the emergency department? A discrete-choice experiment comparing the preferences of parents and healthcare professionals in the UK. ARCHIVES OF DISEASE IN CHILDHOOD, 105(8), 765-771. doi:10.1136/archdischild-2019-318209Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology (Internet publication)
Alharbi, Y., Coenen, F., & Arribas-Bel, D. (2020). Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology. Retrieved from https://crcs.seas.harvard.edu/Evaluating Co-reference Chains based Conversation History in Conversational Question Answering (Conference Paper)
Mandya, A. A., Bollegala, D., & Coenen, F. P. (2020). Evaluating Co-reference Chains based Conversation History in Conversational Question Answering. In L. -M. Nguyen, X. -H. Phan, K. Hasida, & S. Tojo (Eds.), Computational Linguistics (pp. 283-292). Singapore: Springer. doi:10.1007/978-981-15-6168-9_24Secure Third Party Data Clustering Using SecureCL, Φ-Data and Multi-User Order Preserving Encryption (Journal article)
Coenen, F., & Nawal, A. (2020). Secure Third Party Data Clustering Using SecureCL, Φ-Data and Multi-User Order Preserving Encryption. Expert Systems.<i>Do not let the history haunt you</i> - Mitigating Compounding Errors in Conversational Question Answering (Conference Paper)
Mandya, A., O'Neill, J., Bollegala, D., & Coenen, F. (2020). <i>Do not let the history haunt you</i> - Mitigating Compounding Errors in Conversational Question Answering. In PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020) (pp. 2017-2025). Retrieved from https://www.webofscience.com/Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering (Preprint)
Contextualised Graph Attention for Improved Relation Extraction (Preprint)
Contextualised Graph Attention for Improved Relation Extraction (Journal article)
Mandya, A., Bollegala, D., & Coenen, F. (2020). Contextualised Graph Attention for Improved Relation Extraction. Retrieved from http://arxiv.org/abs/2004.10624v1Knowledge Base Enrichment by Relation Learning from Social Tagging Data (Journal article)
Dong, H., Wang, W., Coenen, F., & Huang, K. (2020). Knowledge Base Enrichment by Relation Learning from Social Tagging Data. Information Sciences. doi:10.1016/j.ins.2020.04.002Querrying Encrypted Data in Graph Databases (Conference Paper)
Coenen, F. P., Lisitsa, A., & Aburawi, N. N. (2020). Querrying Encrypted Data in Graph Databases.Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data (Journal article)
Celik, N., O’Brien, F., Brennan, S., Rainbow, R. D., Dart, C., Zheng, Y., . . . Barrett-Jolley, R. (2020). Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data. Communications Biology, 3, 1-10. doi:10.1038/s42003-019-0729-3Evaluating Co-reference Chains Based Conversation History in Conversational Question Answering (Chapter)
Mandya, A., Bollegala, D., & Coenen, F. (2020). Evaluating Co-reference Chains Based Conversation History in Conversational Question Answering. In Computational Linguistics (Vol. 1215, pp. 280-292). Springer Nature. doi:10.1007/978-981-15-6168-9_24Experiments in non-personalized future blood glucose level prediction (Conference Paper)
Bevan, R., & Coenen, F. (2020). Experiments in non-personalized future blood glucose level prediction. In CEUR Workshop Proceedings Vol. 2675 (pp. 100-104).From Semi-automated to Automated Methods of Ontology Learning from Twitter Data (Conference Paper)
Alajlan, S., Coenen, F., & Mandya, A. (2020). From Semi-automated to Automated Methods of Ontology Learning from Twitter Data. In Unknown Conference (pp. 213-236). Springer International Publishing. doi:10.1007/978-3-030-66196-0_10Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (Conference Paper)
Mandya, A., Bollegala, D., & Coenen, F. (2020). Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics. doi:10.18653/v1/2020.coling-main.565In-Bed Human Pose Classification Using Sparse Inertial Signals (Chapter)
Elnaggar, O., Coenen, F., & Paoletti, P. (2020). In-Bed Human Pose Classification Using Sparse Inertial Signals. In Lecture Notes in Computer Science (pp. 331-344). Springer International Publishing. doi:10.1007/978-3-030-63799-6_25Maintaining Curated Document Databases Using a Learning to Rank Model: The ORRCA Experience (Conference Paper)
Muhammad, I., Bollegala, D., Coenen, F., Gamble, C., Kearney, A., & Williamson, P. (2020). Maintaining Curated Document Databases Using a Learning to Rank Model: The ORRCA Experience. In Unknown Conference (pp. 345-357). Springer International Publishing. doi:10.1007/978-3-030-63799-6_26Open Information Extraction for Knowledge Graph Construction (Conference Paper)
Muhammad, I., Kearney, A., Gamble, C., Coenen, F., & Williamson, P. (2020). Open Information Extraction for Knowledge Graph Construction. In Unknown Conference (pp. 103-113). Springer International Publishing. doi:10.1007/978-3-030-59028-4_10Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices (Conference Paper)
Almutairi, N., Coenen, F., & Dures, K. (2020). Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices. In Unknown Conference (pp. 3-24). Springer International Publishing. doi:10.1007/978-3-030-49559-6_12019
Combining Textual and Visual Information for Typed and Handwritten Text Separation in Legal Documents (Conference Paper)
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Celik, N., O’Brien, F., Brennan, S., Rainbow, R., Dart, C., Zheng, Y., . . . Barrett-Jolley, R. (n.d.). Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data. BioRxiv. doi:10.1101/767418A Cryptographic Ensemble for Secure Third Party Data Analysis: Collaborative Data Clustering Without Data Owner Participation (Journal article)
Almutairi, S. T., Coenen, F. P., & Dures, K. (2019). A Cryptographic Ensemble for Secure Third Party Data Analysis: Collaborative Data Clustering Without Data Owner Participation. Knowledge and Data Engineering. doi:10.1016/j.datak.2019.101734Automated Bundle Pagination Using Machine Learning (Conference Paper)
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Leigh, S., Grant, A., Murray, N., Faragher, B., Desai, H., Dolan, S., . . . Carrol, E. D. (2019). The cost of diagnostic uncertainty: a prospective economic analysis of febrile children attending an NHS emergency department. BMC Medicine, 17. doi:10.1186/s12916-019-1275-zModified framework for sarcasm detection and classification in sentiment analysis (Journal article)
Coenen, F. P., Suhaimin, M. S. M., Hijazi, M. H. A., & Alfred, R. (n.d.). Modified framework for sarcasm detection and classification in sentiment analysis. Indonesian Journal of Electrical Engineering and Computer Science. doi:10.11591/ijeecs.v13.i3.pp1175-1183Particle swarm Optimized Density-based Clustering and Classification: Supervised and unsupervised learning approaches (Journal article)
Guan, C., Yuen, K. K. F., & Coenen, F. (2019). Particle swarm Optimized Density-based Clustering and Classification: Supervised and unsupervised learning approaches. Swarm and Evolutionary Computation, 44, 876-896. doi:10.1016/j.swevo.2018.09.008A Dataset for Inter-Sentence Relation Extraction using Distant Supervision (Conference Paper)
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Alshehri, A., Coenen, F., & Bollegala, D. (2019). Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain. In Communications in Computer and Information Science (pp. 43-66). Springer International Publishing. doi:10.1007/978-3-030-15640-4_3Effective Frequent Motif Discovery for Long Time Series Classification: A Study using Phonocardiogram (Conference Paper)
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Coenen, F., & Dittakan, K. (2019). Image Representation for Image Mining: A Study Focusing on Mining Satellite Images for Census Data Collection. In Communications in Computer and Information Science (pp. 3-27). Springer International Publishing. doi:10.1007/978-3-319-99701-8_1Joint Multi-Label Attention Networks for Social Text Annotation (Conference Paper)
Dong, H., Wang, W., Huang, K., & Coenen, F. (2019). Joint Multi-Label Attention Networks for Social Text Annotation. In 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1 (pp. 1348-1354). Retrieved from https://www.webofscience.com/Motif Discovery in Long Time Series: Classifying Phonocardiograms (Conference Paper)
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Alajlan, S., Coenen, F., Konev, B., & Mandya, A. (2019). Ontology Learning from Twitter Data. In KEOD: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 2: KEOD (pp. 94-103). doi:10.5220/0008067600940103Preface (Conference Paper)
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Alshehri, A., Coenen, F., & Bollegala, D. (2018). Iterative Keystroke Continuous Authentication: A Time Series Based Approach. KUNSTLICHE INTELLIGENZ, 32(4), 231-243. doi:10.1007/s13218-018-0526-zPOSTER: A Re-evaluation of Intrusion Detection Accuracy: an Alternative Evaluation Strategy (Conference Paper)
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Abdullahi, F., & Coenen, F. (2018). Multi-Dimensional Banded Pattern Mining.Segmenting sound waves to support Phonocardiogram analysis: the PCGseg Approach (Conference Paper)
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Cui, X., Al-Bazzaz, N., Bollegala, D., & Coenen, F. (2018). A comparative study of pivot selection strategies for unsupervised cross-domain sentiment classification. KNOWLEDGE ENGINEERING REVIEW, 33. doi:10.1017/S0269888918000085Attributes-oriented clothing description and retrieval with multi-task convolutional neural network (Conference Paper)
Xia, Y., Chen, B., Lu, W., Coenen, F., & Zhang, B. (2017). Attributes-oriented clothing description and retrieval with multi-task convolutional neural network. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE. doi:10.1109/fskd.2017.8393378Multilingual and Skew License Plate Detection Based on Extremal Regions (Conference Paper)
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Coenen, F. P., Pratt, H., Zheng, Y., Harding, S., Williams, B., & Broadbent, D. (2018). Automated Diagnosis of Fundus Camera Images for Diabetic Retinopathy for Treatment Referral. European Journal of Ophthalmology.Performance of Case-Based Reasoning Retrieval Using Classification Based on Associations versus Jcolibri and FreeCBR: A Further Validation Study (Conference Paper)
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Pratt, H., Williams, B. M., Ku, J. Y., Vas, C., McCann, E., Al-Bander, B., . . . Zheng, Y. (2018). Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography. JOURNAL OF IMAGING, 4(1). doi:10.3390/jimaging4010004Classifier-Based Pattern Selection Approach for Relation Instance Extraction (Conference Paper)
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Yan, C., Coenen, F., Yue, Y., Yang, X., & Zhang, B. (2016). Video-Based Classification of Driving Behavior Using a Hierarchical Classification System with Multiple Features. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 30(5). doi:10.1142/S0218001416500105Mining frequent itemsets using the N-list and subsume concepts (Journal article)
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Zhang, Y., Zhang, B., Coenen, F., & Lu, W. (2013). Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Machine Vision and Applications, 24(7), 1405-1420. doi:10.1007/s00138-012-0459-8Minimal Vertex Unique Labelled Subgraph Mining (Conference Paper)
Yu, W., Coenen, F., Zito, M., & El Salhi, S. (2013). Minimal Vertex Unique Labelled Subgraph Mining. In Unknown Conference (pp. 317-326). Springer Berlin Heidelberg. doi:10.1007/978-3-642-40131-2_28Population Estimation Mining Using Satellite Imagery (Conference Paper)
Dittakan, K., Coenen, F., Christley, R., & Wardeh, M. (2013). Population Estimation Mining Using Satellite Imagery. In Unknown Conference (pp. 285-296). Springer Berlin Heidelberg. doi:10.1007/978-3-642-40131-2_25Feature Representation for Customer Attrition Risk Prediction in Retail Banking (Conference Paper)
Wang, Y. J., Di, G., Yu, J., Lei, J., & Coenen, F. (2013). Feature Representation for Customer Attrition Risk Prediction in Retail Banking. In Unknown Conference (pp. 229-238). Springer Berlin Heidelberg. doi:10.1007/978-3-642-39736-3_18Satellite Image Mining for Census Collection: A Comparative Study with Respect to the Ethiopian Hinterland (Journal article)
Dittakan, K., Coenen, F., & Christley, R. (2013). Satellite Image Mining for Census Collection: A Comparative Study with Respect to the Ethiopian Hinterland. Unknown Journal, 260-274. doi:10.1007/978-3-642-39712-7_20Extracting debate graphs from parliamentary transcripts (Conference Paper)
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Vo, B., Coenen, F., & Le, B. (2013). A new method for mining Frequent Weighted Itemsets based on WIT-trees. Expert Systems with Applications, 40(4), 1256-1264. doi:10.1016/j.eswa.2012.08.065A survey of frequent subgraph mining algorithms (Journal article)
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Dittakan, K., Coenen, F., Christley, R., & Wardeh, M. (2013). A Comparative Study of Three Image Representations for Population Estimation Mining Using Remote Sensing Imagery. In ADMA'13 (pp. TBA). Hangzhou, China: Springer.A Hybrid Approach for Mining Frequent Itemsets (Conference Paper)
Vo, B., Le, T., Coenen, F., & Hong, T. -P. (2013). A Hybrid Approach for Mining Frequent Itemsets. In SMC 2013 (pp. TBA). Manchester, UK.: IEEE.A Multiagent Based Framework for the Simulation of Mammalian Behaviour (Conference Paper)
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Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2013). Age-related macular degeneration screening using data mining approaches.. In 1st International Conference on Aritificial Intelligence, Modelling and Simulation (pp. 1-8). Kota Kinabalu: IEEE.An Efficient Algorithm for Mining Erasable Itemsets Using the Difference of NC-Sets (Conference Paper)
Le, T., Vo, B., & Coenen, F. (2013). An Efficient Algorithm for Mining Erasable Itemsets Using the Difference of NC-Sets. In SMC'13 (pp. TBA). Manchester: IEEE.An Inductive Rule Learning Technique for Text Mining in Questionnaires (Conference Paper)
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Albarrak, A., Coenen, F., & Zheng, Y. (2013). Classification of Volumetric Retinal Images Using Overlapping Decomposition and Tree Analysis. In 2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) (pp. 11-16). Retrieved from https://www.webofscience.com/Classification of Volumetric Retinal Images Using Overlapping Decomposition and Tree Analysis (Conference Paper)
Albarrak, A., Coenen, F., & Zheng, Y. (2013). Classification of Volumetric Retinal Images Using Overlapping Decomposition and Tree Analysis. In IEEE CBMS2013 (pp. 1-8). Porto: IEEE.Classification of volumetric retinal images using overlapping decomposition and tree analysis (Conference Paper)
Albarrak, A., Coenen, F., & Zheng, Y. (2013). Classification of volumetric retinal images using overlapping decomposition and tree analysis. In The 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 6). Porto, Portugal: IEEE.Generating Domain-Specific Sentiment Lexicons for Opinion Mining (Conference Paper)
Salah, Z., Coenen, F., & Grossi, D. (2013). Generating Domain-Specific Sentiment Lexicons for Opinion Mining. In ADMA'13 (pp. TBA). Hangzhou, China: Soringer.Hierarchical Classification for Solving Multi-class Problems: A New Approach Using Naive Bayesian Classification (Conference Paper)
Alshdaifat, E., Coenen, F., & Dures, K. (2013). Hierarchical Classification for Solving Multi-class Problems: A New Approach Using Naive Bayesian Classification. In ADMA'13 (pp. TBA). Hangzhou, China: Springer.Hierarchical Single Label Classification: An Alternative Approach (Chapter)
Alshdaifat, E., Coenen, F., & Dures, K. (2013). Hierarchical Single Label Classification: An Alternative Approach. In Research and Development in Intelligent Systems XXX (pp. 39-52). Springer International Publishing. doi:10.1007/978-3-319-02621-3_3Hierarchical Single Label classification: An Alternative Approach (Conference Paper)
Alshdaifat, E., Coenen, F., & Dures, K. (2013). Hierarchical Single Label classification: An Alternative Approach. In BCS-SGAI AI'2013 (pp. TBA). Cambridge, UK: Springer.Predicting Features in Complex 3D Surfaces Using a Point Series Representation: A Case Study in Sheet Metal Forming (Conference Paper)
El Salhi, S., Coenen, F., Dixon, C., & Khan, M. (2013). Predicting Features in Complex 3D Surfaces Using a Point Series Representation: A Case Study in Sheet Metal Forming. In ADMA'13 (pp. TBA). Hangzhou, China: Springer.Satellite Image Mining for Census Collection: A Comparative Study With Respect to the Ethiopian Hinterland (Conference Paper)
Dittakan, K., Coenen, F., & Christley, R. (2013). Satellite Image Mining for Census Collection: A Comparative Study With Respect to the Ethiopian Hinterland. In MLDM'13 (pp. 260-274). New York: Springer LNAI 7988.Vertex Unique Labelled Subgraph Mining (Conference Paper)
Wen, Y., Coenen, F., Zito, M., & Salhi, S. (2013). Vertex Unique Labelled Subgraph Mining. In BCS-SGSI AI 20-13 (pp. TBA). Cambridge, UK: Springer.Vertex Unique Labelled Subgraph Mining (Chapter)
Yu, W., Coenen, F., Zito, M., & Salhi, S. E. (2013). Vertex Unique Labelled Subgraph Mining. In Research and Development in Intelligent Systems XXX (pp. 21-37). Springer International Publishing. doi:10.1007/978-3-319-02621-3_2Vertex Unique Labelled Subgraph Mining for Vertex Label Classification (Conference Paper)
Yu, W., Coenen, F., & Zito, M. (2013). Vertex Unique Labelled Subgraph Mining for Vertex Label Classification. In ADMA'13 (pp. TBA). Hangzhou, China: Springer.2012
Automated "Disease/No Disease" Grading of Age-Related Macular Degeneration by an Image Mining Approach (Journal article)
Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated "Disease/No Disease" Grading of Age-Related Macular Degeneration by an Image Mining Approach. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 53(13), 8310-8318. doi:10.1167/iovs.12-9576An investigation into the issues of multi-agent data mining (Thesis / Dissertation)
Albashiri, K. A., Coenen, F., & Leng, P. (2012). An investigation into the issues of multi-agent data mining.Volumetric Image Mining Based on Decomposition and Graph Analysis: An Application to Retinal Optical Coherence Tomography (Conference Paper)
Albarrak, A., Coenen, F., Zheng, Y., & Yu, W. (2012). Volumetric Image Mining Based on Decomposition and Graph Analysis: An Application to Retinal Optical Coherence Tomography. In 13TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI 2012) (pp. 263-268). Retrieved from https://www.webofscience.com/A framework for Multi-Agent Based Clustering (Journal article)
Chaimontree, S., Atkinson, K., & Coenen, F. (2012). A framework for Multi-Agent Based Clustering. Autonomous Agents and Multi-Agent Systems, 25(3), 425-446. doi:10.1007/s10458-011-9187-0Multi-agent based classification using argumentation from experience (Journal article)
Wardeh, M., Coenen, F., & Bench-Capon, T. (2012). Multi-agent based classification using argumentation from experience. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 25(3), 447-474. doi:10.1007/s10458-012-9197-6Identification and Visualisation of Pattern Migrations in Big Network Data (Conference Paper)
Nohuddin, P. N. E., Coenen, F., Christley, R., & Sunayama, W. (2012). Identification and Visualisation of Pattern Migrations in Big Network Data. In Unknown Conference (pp. 883-886). Springer Berlin Heidelberg. doi:10.1007/978-3-642-32695-0_91Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization (Chapter)
Elsayed, A., Coenen, F., Garca-Fiana, M., & Sluming, V. (n.d.). Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization. In Data Mining Applications in Engineering and Medicine. InTech. doi:10.5772/50019Highly reliable breast cancer diagnosis with cascaded ensemble classifiers (Conference Paper)
Yungang Zhang., Bailing Zhang., Coenenz, F., & Wenjin Lu. (2012). Highly reliable breast cancer diagnosis with cascaded ensemble classifiers. In The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE. doi:10.1109/ijcnn.2012.6252547A Semi-Automated Approach to Building Text Summarisation Classifiers (Conference Paper)
Garcia-Constantino, M., Coenen, F., Noble, P. -J., Radford, A., & Setzkorn, C. (2012). A Semi-Automated Approach to Building Text Summarisation Classifiers. In Unknown Conference (pp. 495-509). Springer Berlin Heidelberg. doi:10.1007/978-3-642-31537-4_39Finding Correlations between 3-D Surfaces: A Study in Asymmetric Incremental Sheet Forming (Conference Paper)
Khan, M. S., Coenen, F., Dixon, C., & El-Salhi, S. (2012). Finding Correlations between 3-D Surfaces: A Study in Asymmetric Incremental Sheet Forming. In Unknown Conference (pp. 366-379). Springer Berlin Heidelberg. doi:10.1007/978-3-642-31537-4_29Data mining techniques for the screening of age-related macular degeneration (Journal article)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2012). Data mining techniques for the screening of age-related macular degeneration. KNOWLEDGE-BASED SYSTEMS, 29, 83-92. doi:10.1016/j.knosys.2011.07.002Finding "interesting" trends in social networks using frequent pattern mining and self organizing maps (Journal article)
Nohuddin, P. N. E., Coenen, F., Christley, R., Setzkorn, C., Patel, Y., & Williams, S. (2012). Finding "interesting" trends in social networks using frequent pattern mining and self organizing maps. KNOWLEDGE-BASED SYSTEMS, 29, 104-113. doi:10.1016/j.knosys.2011.07.003Finding “interesting” trends in social networks using frequent pattern mining and self organizing maps (Journal article)
Nohuddin, P. N. E., Coenen, F., Christley, R., Setzkorn, C., Patel, Y., & Williams, S. (2012). Finding “interesting” trends in social networks using frequent pattern mining and self organizing maps. Knowledge-Based Systems, 29, 104-113. doi:10.1016/j.knosys.2011.07.003PISA: A framework for multiagent classification using argumentation (Journal article)
Wardeh, M., Coenen, F., & Capon, T. B. (2012). PISA: A framework for multiagent classification using argumentation. DATA & KNOWLEDGE ENGINEERING, 75, 34-57. doi:10.1016/j.datak.2012.03.001A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents (Conference Paper)
Chaimontree, S., Atkinson, K., & Coenen, F. (2012). A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents. In Unknown Conference (pp. 16-29). Springer Berlin Heidelberg. doi:10.1007/978-3-642-27609-5_3A Semi-Automated Approach to Building Text Summarisation Classifiers (Journal article)
Garcia-Constantino, M., Coenen, F., Noble, P. -J., & Radford, A. (2012). A Semi-Automated Approach to Building Text Summarisation Classifiers. Journal of Theoretical and Applied Computer Science.Classification Based 3-D Surface Analysis: Predicting Springback in Sheet Metal Forming (Journal article)
Khan, M. S., Coenen, F., Dixon, C., & El-Salhi, S. (2012). Classification Based 3-D Surface Analysis: Predicting Springback in Sheet Metal Forming. Journal of Theoretical and Applied Computer Science, 6(2), 45-59.Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming (Conference Paper)
El-Salhi, S., Coenen, F., Dixon, C., & Khan, M. S. (2012). Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming. In Unknown Conference (pp. 391-404). Springer London. doi:10.1007/978-1-4471-4739-8_30Image Mining Approaches for The Screening of Age-Related Macular Degeneration (Chapter)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2012). Image Mining Approaches for The Screening of Age-Related Macular Degeneration. In Data Mining (Working Title) (pp. TBA). Hauppauge (NY), USA: Nova Sciences Publishers Inc..Image mining approaches for the screening of age-related macular degeneration (Chapter)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2012). Image mining approaches for the screening of age-related macular degeneration. In Retinopathy: New Research (pp. 101-142).Questionnaire Free Text Summarisation Using Hierarchical Classification (Conference Paper)
Garcia-Constantino, M., Coenen, F., Noble, P. -J., & Radford, A. (2012). Questionnaire Free Text Summarisation Using Hierarchical Classification. In Unknown Conference (pp. 35-48). Springer London. doi:10.1007/978-1-4471-4739-8_3Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization (Chapter)
Elsayed, A., Coenen, F., GarcÃa-Fiñana, M., & Sluming, V. (2012). Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization. In A. Karahoca (Ed.), Data Mining Applications in Engineering and Medicine (pp. 225-248). Slavka Krautzeka, Croatia: InTech - Open Science.Towards The Collection of Census Data From Satellite Imagery Using Data Mining: A Study With Respect to the Ethiopian Hinterland (Conference Paper)
Dittakan, K., Coenen, F., & Christley, R. (2012). Towards The Collection of Census Data From Satellite Imagery Using Data Mining: A Study With Respect to the Ethiopian Hinterland. In Unknown Conference (pp. 405-418). Springer London. doi:10.1007/978-1-4471-4739-8_312011
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Elsayed, A., Hijazi, M. H. A., Coenen, F., Garcia-Finana, M., Sluming, V., & Zheng, Y. (2011). Time Series Case Based Reasoning for Image Categorisation. In CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2011 Vol. 6880 (pp. 423-+). Retrieved from https://www.webofscience.com/Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining (Conference Paper)
Hijazi, M. H. A., Jiang, C., Coenen, F., & Zheng, Y. (2011). Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining. In MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT II Vol. 6912 (pp. 65-80). Retrieved from https://www.webofscience.com/An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data (Conference Paper)
Garcia-Constantino, M., Coenen, F., Noble, P. -J., Radford, A., Setzkorn, C., & Tierney, A. (2011). An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data. In Unknown Conference (pp. 387-398). Springer Berlin Heidelberg. doi:10.1007/978-3-642-23199-5_29Incremental Web-Site Boundary Detection Using Random Walks (Conference Paper)
Alshukri, A., Coenen, F., & Zito, M. (2011). Incremental Web-Site Boundary Detection Using Random Walks. In Unknown Conference (pp. 414-427). Springer Berlin Heidelberg. doi:10.1007/978-3-642-23199-5_31Finding Associations in Composite Data Sets (Journal article)
Khan, M. S., Muyeba, M., Coenen, F., Reid, D., & Tawfik, H. (2011). Finding Associations in Composite Data Sets. International Journal of Data Warehousing and Mining, 7(3), 1-29. doi:10.4018/jdwm.2011070101Arguing from experience using multiple groups of agents (Journal article)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2011). Arguing from experience using multiple groups of agents. Argument & Computation, 2(1), 51-76. doi:10.1080/19462166.2010.528176Data mining: past, present and future (Journal article)
Coenen, F. (n.d.). Data mining: past, present and future. The Knowledge Engineering Review, 26(1), 25-29. doi:10.1017/s0269888910000378A Comparative Study of Using CARM Approaches in Mesenchymal Stem Cell Differentiation Analysis (Chapter)
Wang, W., Wang, Y., Bañares-Alcántara, R., Cui, Z., & Coenen, F. P. (2011). A Comparative Study of Using CARM Approaches in Mesenchymal Stem Cell Differentiation Analysis. In A. V. Kumar Senthil (Ed.), Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains (pp. 223-243). Hershey (PA), USA: IGI Global.A multi-agent based approach to clustering: Harnessing the power of agents (Conference Paper)
Chaimontree, S., Atkinson, K., & Coenen, F. (2011). A multi-agent based approach to clustering: Harnessing the power of agents. In Seventh International Workshop on Agents and Data Mining Interaction (pp. 103-114). Taiwan: Springer LNCS 5980.Automated grading of age-related macular degeneration by an image mining approach (Conference Paper)
Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2011). Automated grading of age-related macular degeneration by an image mining approach. In Inv Ophth Vis Sci Vol. 52 (pp. 6568).Classification of MRI Brain Scan Data Using Shape Criteria (Journal article)
Elsayed, A., Hijazi, M. H. A., Coenen, F., Garcia-Finana, M., Sluming, V., & Zheng, Y. (2011). Classification of MRI Brain Scan Data Using Shape Criteria. Annals of the British Machine Vision Association (BMVA), 2011(6), 1-14. Retrieved from http://www.bmva.org/annals/2011/2011-0006.pdfFinding Associations in Composite Data Sets: The CFARM Algorithm (Journal article)
Khan, M. S., Muyeba, M. K., Coenen, F., Reid, D., & Tawfik, H. (2011). Finding Associations in Composite Data Sets: The CFARM Algorithm. Journal of Data Warehousing and Mining, 7(31), 1-29.Identifying Age-related Macular Degeneration In Volumetric Retinal Images. (Conference Paper)
Albarrak, A., Coenen, F., & Zheng, Y. (2011). Identifying Age-related Macular Degeneration In Volumetric Retinal Images.. In Ophthalmic Image Analysis Workshop (pp. 53-58). Liverpool: University of Liverpool.Multi-agent Based Classification Using Argumentation from Experience (Conference Paper)
Wardeh, M., Coenen, F., Bench-Capon, T., & Wyner, A. (2011). Multi-agent Based Classification Using Argumentation from Experience. In ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PT II Vol. 6635 (pp. 357-369). Retrieved from https://www.webofscience.com/Retinal Image Classification for the Screening of Age-Related Macular Degeneration (Conference Paper)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2011). Retinal Image Classification for the Screening of Age-Related Macular Degeneration. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVIII (pp. 325-338). doi:10.1007/978-0-85729-130-1_25Retinal image classification for the screening of age-related macular degeneration (Conference Paper)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2011). Retinal image classification for the screening of age-related macular degeneration. In Res. and Dev. in Intelligent Syst. XXVII: Incorporating Applications and Innovations in Intel. Sys. XVIII - AI 2010, 30th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 325-338). doi:10.1007/978-0-85729-130-1_25Rule Learning with Negation for Text Classification (Conference Paper)
Chua, S., Coenen, F., & Malcom, G. (2011). Rule Learning with Negation for Text Classification. In MLDM 2011 poster proceedings (pp. 1-14). New York: ibai-publisging.SOMA: A Proposed Framework for Trend Mining in Large UK Diabetic Retinopathy Temporal Databases (Conference Paper)
Somaraki, V., Harding, S., Broadbent, D., & Coenen, F. (2011). SOMA: A Proposed Framework for Trend Mining in Large UK Diabetic Retinopathy Temporal Databases. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVIII (pp. 285-290). doi:10.1007/978-0-85729-130-1_22SOMA: A proposed framework for trend mining in large UK diabetic retinopathy temporal databases (Conference Paper)
Somaraki, V., Harding, S., Broadbent, D., & Coenen, F. (2011). SOMA: A proposed framework for trend mining in large UK diabetic retinopathy temporal databases. In Res. and Dev. in Intelligent Syst. XXVII: Incorporating Applications and Innovations in Intel. Sys. XVIII - AI 2010, 30th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 285-290). doi:10.1007/978-0-85729-130-1_22Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps (Conference Paper)
Nohuddin, P. N. E., Christley, R., Coenen, F., Patel, Y., Setzkorn, C., & Williams, S. (2011). Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVIII (pp. 311-324). doi:10.1007/978-0-85729-130-1_24Social network trend analysis using frequent pattern mining and self organizing maps (Conference Paper)
Nohuddin, P. N. E., Christley, R., Coenen, F., Patel, Y., Setzkorn, C., & Williams, S. (2011). Social network trend analysis using frequent pattern mining and self organizing maps. In Res. and Dev. in Intelligent Syst. XXVII: Incorporating Applications and Innovations in Intel. Sys. XVIII - AI 2010, 30th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 311-324). doi:10.1007/978-0-85729-130-1_24The Application of AI Techniques to Deformation in Metal Manufacturing (Conference Paper)
Dixon, C., Coenen, F., & Khan, M. (2011). The Application of AI Techniques to Deformation in Metal Manufacturing. In WAR (pp. 17-18). Glasgow: University of Glasgow.Towards Large-Scale Multi-Agent Based Rodent Simulation: The "Mice In A Box" Scenario (Conference Paper)
Agiriga, E., Coenen, F., Hurst, J., Beynon, R., & Kowalski, D. (2011). Towards Large-Scale Multi-Agent Based Rodent Simulation: The "Mice In A Box" Scenario. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVIII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XIX (pp. 369-+). doi:10.1007/978-1-4471-2318-7_28Towards large-scale multi-agent based rodent simulation: The "mice in a box" scenario (Conference Paper)
Agiriga, E., Coenen, F., Hurst, J., Beynon, R., & Kowalski, D. (2011). Towards large-scale multi-agent based rodent simulation: The "mice in a box" scenario. In Res. and Dev. in Intelligent Syst. XXVIII: Incorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 369-382). doi:10.1007/978-1-4471-2318-7_28Trend Mining and Visualisation in Social Networks (Conference Paper)
Nohuddin, P. N. E., Sunayama, W., Christley, R., Coenen, F., & Setzkorn, C. (2011). Trend Mining and Visualisation in Social Networks. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVIII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XIX (pp. 269-+). doi:10.1007/978-1-4471-2318-7_21Trend mining and visualisation in social networks (Conference Paper)
Nohuddin, P. N. E., Sunayama, W., Christley, R., Coenen, F., & Setzkorn, C. (2011). Trend mining and visualisation in social networks. In Res. and Dev. in Intelligent Syst. XXVIII: Incorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 269-282). doi:10.1007/978-1-4471-2318-7_21Using Negation and Phrases in Inducing Rules for Text Classification (Conference Paper)
Chua, S., Coenen, F., Malcolm, G., Fernando, M., & Constantino, G. (2011). Using Negation and Phrases in Inducing Rules for Text Classification. In Unknown Conference (pp. 153-166). Springer London. doi:10.1007/978-1-4471-2318-7_11Web-Site Boundary Detection Using Incremental Random Walk Clustering (Conference Paper)
Alshukri, A., Coenen, F., & Zito, M. (2011). Web-Site Boundary Detection Using Incremental Random Walk Clustering. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVIII: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XIX (pp. 255-268). doi:10.1007/978-1-4471-2318-7_20Web-site boundary detection using incremental randomwalk clustering (Conference Paper)
Alshukri, A., Coenen, F., & Zito, M. (2011). Web-site boundary detection using incremental randomwalk clustering. In Res. and Dev. in Intelligent Syst. XXVIII: Incorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 255-268). doi:10.1007/978-1-4471-2318-7_202010
Multi-Party Argument from Experience (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2010). Multi-Party Argument from Experience. In ARGUMENTATION IN MULTI-AGENT SYSTEMS Vol. 6057 (pp. 216-235). Retrieved from https://www.webofscience.com/Best Clustering Configuration Metrics: Towards Multiagent Based Clustering (Conference Paper)
Chaimontree, S., Atkinson, K., & Coenen, F. (2010). Best Clustering Configuration Metrics: Towards Multiagent Based Clustering. In Unknown Conference (pp. 48-59). Springer Berlin Heidelberg. doi:10.1007/978-3-642-17316-5_5Classification Inductive Rule Learning with Negated Features (Conference Paper)
Chua, S., Coenen, F., & Malcolm, G. (2010). Classification Inductive Rule Learning with Negated Features. In Unknown Conference (pp. 125-136). Springer Berlin Heidelberg. doi:10.1007/978-3-642-17316-5_12Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach (Conference Paper)
Jiang, C., Coenen, F., & Zito, M. (2010). Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach. In ADVANCED DATA MINING AND APPLICATIONS, ADMA 2010, PT I Vol. 6440 (pp. 405-416). Retrieved from https://www.webofscience.com/Frequent Pattern Trend Analysis in Social Networks (Conference Paper)
Nohuddin, P. N. E., Christley, R., Coenen, F., Patel, Y., Setzkorn, C., & Williams, S. (2010). Frequent Pattern Trend Analysis in Social Networks. In ADVANCED DATA MINING AND APPLICATIONS, ADMA 2010, PT I Vol. 6440 (pp. 358-369). Retrieved from https://www.webofscience.com/An association rule-based CLIPS program for interactive prediction of MSC differentiation in vitro (Conference Paper)
Wang, W., Banares-Alcantara, R., Cui, Z., Wang, Y. J., & Coenen, F. (2010). An association rule-based CLIPS program for interactive prediction of MSC differentiation in vitro. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE. doi:10.1109/iccasm.2010.5620726Frequent Sub-graph Mining on Edge Weighted Graphs (Conference Paper)
Jiang, C., Coenen, F., & Zito, M. (2010). Frequent Sub-graph Mining on Edge Weighted Graphs. In DATA WAREHOUSING AND KNOWLEDGE DISCOVERY Vol. 6263 (pp. 77-88). Retrieved from https://www.webofscience.com/Region of Interest Based Image Categorization (Chapter)
Elsayed, A., Coenen, F., García-Fiñana, M., & Sluming, V. (2010). Region of Interest Based Image Categorization. In Data Warehousing and Knowledge Discovery (pp. 239-250). Springer Berlin Heidelberg. doi:10.1007/978-3-642-15105-7_19Clustering in a Multi-Agent Data Mining Environment (Conference Paper)
Chaimontree, S., Atkinson, K., & Coenen, F. (2010). Clustering in a Multi-Agent Data Mining Environment. In Unknown Conference (pp. 103-114). Springer Berlin Heidelberg. doi:10.1007/978-3-642-15420-1_9Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy (Conference Paper)
Somaraki, V., Broadbent, D., Coenen, F., & Harding, S. (2010). Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy. In ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS Vol. 6171 (pp. 418-+). Retrieved from https://www.webofscience.com/Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification (Conference Paper)
Wang, Y. J., Li, F., Coenen, F., Sanderson, R., & Xin, Q. (2010). Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification. In Unknown Conference (pp. 222-236). Springer Berlin Heidelberg. doi:10.1007/978-3-642-14400-4_18Image Classification Using Histograms and Time Series Analysis: A Study of Age-Related Macular Degeneration Screening in Retinal Image Data (Conference Paper)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2010). Image Classification Using Histograms and Time Series Analysis: A Study of Age-Related Macular Degeneration Screening in Retinal Image Data. In ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS Vol. 6171 (pp. 197-+). Retrieved from https://www.webofscience.com/Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining (Conference Paper)
Chaimontree, S., Atkinson, K., & Coenen, F. (2010). Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining. In Unknown Conference (pp. 115-127). Springer Berlin Heidelberg. doi:10.1007/978-3-642-14400-4_9Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database (Conference Paper)
Nohuddin, P. N. E., Christley, R., Coenen, F., & Setzkorn, C. (2010). Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database. In ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS Vol. 6171 (pp. 464-+). Retrieved from https://www.webofscience.com/Web-Site Boundary Detection (Conference Paper)
Alshukri, A., Coenen, F., & Zito, M. (2010). Web-Site Boundary Detection. In ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS Vol. 6171 (pp. 529-543). Retrieved from https://www.webofscience.com/A sliding windows based dual support framework for discovering emerging trends from temporal data (Journal article)
Khan, M. S., Coenen, F., Reid, D., Patel, R., & Archer, L. (2010). A sliding windows based dual support framework for discovering emerging trends from temporal data. Knowledge-Based Systems, 23(4), 316-322. doi:10.1016/j.knosys.2009.11.005Corpus callosum MR image classification (Journal article)
Elsayed, A., Coenen, F., Jiang, C., García-Fiñana, M., & Sluming, V. (2010). Corpus callosum MR image classification. Knowledge-Based Systems, 23(4), 330-336. doi:10.1016/j.knosys.2009.11.008Text classification using graph mining-based feature extraction (Journal article)
Jiang, C., Coenen, F., Sanderson, R., & Zito, M. (2010). Text classification using graph mining-based feature extraction. KNOWLEDGE-BASED SYSTEMS, 23(4), 302-308. doi:10.1016/j.knosys.2009.11.010A Sliding Windows based Dual Support Framework for Discovering Emerging Trends from Temporal Data (Conference Paper)
Khan, M. S., Coenen, F., Reid, D., Patel, R., & Archer, L. (2010). A Sliding Windows based Dual Support Framework for Discovering Emerging Trends from Temporal Data. In Unknown Conference (pp. 35-48). Springer London. doi:10.1007/978-1-84882-983-1_3Arguing in Groups (Conference Paper)
Wardeh, M., Coenen, F., & Bench-Capon, T. (2010). Arguing in Groups. In COMPUTATIONAL MODELS OF ARGUMENT: PROCEEDINGS OF COMMA 2010 Vol. 216 (pp. 475-486). doi:10.3233/978-1-60750-619-5-475Corpus Callosum MR Image Classification (Conference Paper)
Elsayed, A., Coenen, F., Jiang, C., García-Fiñana, M., & Sluming, V. (2010). Corpus Callosum MR Image Classification. In Unknown Conference (pp. 333-346). Springer London. doi:10.1007/978-1-84882-983-1_27Detecting Temporal Pattern and Cluster Changes in Social Networks: A Study Focusing UK Cattle Movement Database (Conference Paper)
Nohuddin, P. N. E., Coenen, F., Christley, R., & Setzkorn, C. (2010). Detecting Temporal Pattern and Cluster Changes in Social Networks: A Study Focusing UK Cattle Movement Database. In INTELLIGENT INFORMATION PROCESSING V Vol. 340 (pp. 163-+). doi:10.1007/978-3-642-16327-2_22Detecting temporal pattern and cluster changes in social networks: A study focusing UK cattle movement database (Conference Paper)
Nohuddin, P. N. E., Coenen, F., Christley, R., & Setzkorn, C. (2010). Detecting temporal pattern and cluster changes in social networks: A study focusing UK cattle movement database. In IFIP Advances in Information and Communication Technology Vol. 340 AICT (pp. 163-172). doi:10.1007/978-3-642-16327-2_22Image categorisation using time series case based reasoning (Conference Paper)
Elsayed, A., Hijazi, M. H. A., Coenen, F., Garcia-Finana, M., Sluming, V., & Zheng, Y. (2010). Image categorisation using time series case based reasoning. In UKCBR10 (pp. 2-11). Cambridge: BCS-SGAI.Region of Interest Based Image Classification using time series analysis (Conference Paper)
Elsayed, A., Coenen, F., Garcia-Finana, M., & Sluming, V. (2010). Region of Interest Based Image Classification using time series analysis. In The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE. doi:10.1109/ijcnn.2010.5596324Retinal Image Classification using a Histogram Based Approach (Conference Paper)
Hijazi, M. H. A., Coenen, F., & Zheng, Y. (2010). Retinal Image Classification using a Histogram Based Approach. In 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010. Retrieved from https://www.webofscience.com/Rule Learning with Negation: Issues Regarding Effectiveness (Conference Paper)
Chua, S., Coenen, F., & Malcolm, G. (2010). Rule Learning with Negation: Issues Regarding Effectiveness. In Unknown Conference (pp. 193-202). Springer Berlin Heidelberg. doi:10.1007/978-3-642-16327-2_25Text Classification using Graph Mining-based Feature Extraction (Conference Paper)
Jiang, C., Coenen, F., Sanderson, R., & Zito, M. (2010). Text Classification using Graph Mining-based Feature Extraction. In RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI (pp. 21-34). doi:10.1007/978-1-84882-983-1_2Text classification using graph mining-based feature extraction (Conference Paper)
Jiang, C., Coenen, F., Sanderson, R., & Zito, M. (2010). Text classification using graph mining-based feature extraction. In Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII (pp. 21-34). doi:10.1007/978-1-84882-983-1_2Trend Mining in Logitudinal Diabetic Retinopathy Data (Conference Paper)
Somaraki, V., Broadbent, D., Coenen, F. P., & Harding, S. (2010). Trend Mining in Logitudinal Diabetic Retinopathy Data. In BCS SGAI conference on AI (pp. 285-290). UK: Springer.2009
Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining. (Journal article)
Wang, W., Wang, Y. J., Bañares-Alcántara, R., Coenen, F., & Cui, Z. (2009). Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.. Journal of bioinformatics and computational biology, 7(6), 905-930. doi:10.1142/s0219720009004424Application of Classification Association Rule Mining for Mammalian Mesenchymal Stem Cell Differentiation (Conference Paper)
Wang, W., Wang, Y. J., Bañares-Alcántara, R., Cui, Z., & Coenen, F. (2009). Application of Classification Association Rule Mining for Mammalian Mesenchymal Stem Cell Differentiation. In Unknown Conference (pp. 51-61). Springer Berlin Heidelberg. doi:10.1007/978-3-642-03067-3_6Effective Mining of Weighted Fuzzy Association Rules (Chapter)
Muyeba, M., Khan, M. S., & Coenen, F. (2010). Effective Mining of Weighted Fuzzy Association Rules. In Rare Association Rule Mining and Knowledge Discovery (pp. 47-64). IGI Global. doi:10.4018/978-1-60566-754-6.ch004Integrating Data Mining and Agent Based Modeling and Simulation (Conference Paper)
Baqueiro, O., Wang, Y. J., McBurney, P., & Coenen, F. (2009). Integrating Data Mining and Agent Based Modeling and Simulation. In Unknown Conference (pp. 220-231). Springer Berlin Heidelberg. doi:10.1007/978-3-642-03067-3_18The EMADS Extendible Multi-Agent Data Mining Framework (Chapter)
Albashiri, K. A., & Coenen, F. (2009). The EMADS Extendible Multi-Agent Data Mining Framework. In Data Mining and Multi-agent Integration (pp. 189-200). Springer US. doi:10.1007/978-1-4419-0522-2_13A Generic and Extendible Multi-Agent Data Mining Framework (Conference Paper)
Albashiri, K. A., & Coenen, F. (2009). A Generic and Extendible Multi-Agent Data Mining Framework. In Unknown Conference (pp. 203-210). Springer Berlin Heidelberg. doi:10.1007/978-3-642-02319-4_24A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification (Conference Paper)
Wang, Y. J., Coenen, F., & Sanderson, R. (2009). A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification. In Unknown Conference (pp. 338-349). Springer Berlin Heidelberg. doi:10.1007/978-3-642-03348-3_33EMADS: An extendible multi-agent data miner (Journal article)
Albashiri, K. A., Coenen, F., & Leng, P. (2009). EMADS: An extendible multi-agent data miner. Knowledge-Based Systems, 22(7), 523-528. doi:10.1016/j.knosys.2008.10.009Arguing from Experience to Classifying Noisy Data (Conference Paper)
Wardeh, M., Coenen, F., & Bench-Capon, T. (2009). Arguing from Experience to Classifying Noisy Data. In DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS Vol. 5691 (pp. 354-365). Retrieved from https://www.webofscience.com/Agent-Enriched Data Mining Using an Extendable Framework (Conference Paper)
Albashiri, K. A., & Coenen, F. (2009). Agent-Enriched Data Mining Using an Extendable Framework. In Unknown Conference (pp. 53-68). Springer Berlin Heidelberg. doi:10.1007/978-3-642-03603-3_5PADUA: a protocol for argumentation dialogue using association rules (Journal article)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2009). PADUA: a protocol for argumentation dialogue using association rules. Artificial Intelligence and Law, 17(3), 183-215. doi:10.1007/s10506-009-9078-8Improved methods for extracting frequent itemsets from interim‐support trees (Journal article)
Coenen, F., Leng, P., Pagourtzis, A., Rytter, W., & Souliou, D. (2009). Improved methods for extracting frequent itemsets from interim‐support trees. Software: Practice and Experience, 39(6), 551-571. doi:10.1002/spe.902A Framework for Mining Fuzzy Association Rules from Composite Items (Conference Paper)
Muyeba, M., Khan, M. S., & Coenen, F. (2009). A Framework for Mining Fuzzy Association Rules from Composite Items. In Unknown Conference (pp. 62-74). Springer Berlin Heidelberg. doi:10.1007/978-3-642-00399-8_6A Histogram Based Approach to Screening of Age-related Macular Degeneration (Conference Paper)
Hijazi, M. H. A., Coenen, F. P., & Zheng, Y. (2009). A Histogram Based Approach to Screening of Age-related Macular Degeneration. In MIUA'09 (pp. 54-158). Warwick: MIUA.Agent-Enriched Data Mining Using an Extendable Framework (Conference Paper)
Albashiri, K. A., & Coenen, F. P. (2009). Agent-Enriched Data Mining Using an Extendable Framework. In ADMI (pp. 89-106). Budapest: AAMAS.An investigation into the issues of Multi-Agent Data Mining (Chapter)
Albashiri, K. A., & Coenen, F. P. (2009). An investigation into the issues of Multi-Agent Data Mining. In D. Bouça, & A. Gafagnão (Eds.), Agent Based Comnputing (pp. 1-85). Hauppauge, NY, USA: Nova Science Publishers.Construction and Application of a Public-Domain Mesenchymal Stem Cell Database (Conference Paper)
Wang, W., Bañares-Alcántara, R., Cui, Z., Wang, Y., & Coenen, F. P. (2009). Construction and Application of a Public-Domain Mesenchymal Stem Cell Database. In ISBME'09 (pp. 78). Bangkok: IEEE.EMADS: An Extendible Multi-Agent Data Miner (Conference Paper)
Albashiri, K. A., Coenen, F., & Leng, P. (2009). EMADS: An Extendible Multi-Agent Data Miner. In Unknown Conference (pp. 263-275). Springer London. doi:10.1007/978-1-84882-171-2_19Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework (Conference Paper)
Muyeba, M., Khan, M. S., & Coenen, F. (2009). Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework. In Unknown Conference (pp. 49-61). Springer Berlin Heidelberg. doi:10.1007/978-3-642-00399-8_5Graph-based Image Classification by Weighting Scheme (Conference Paper)
Jiang, C., & Coenen, F. (2009). Graph-based Image Classification by Weighting Scheme. In Unknown Conference (pp. 63-76). Springer London. doi:10.1007/978-1-84882-215-3_5Mining Allocating Patterns in Investment Portfolios (Chapter)
Wang, Y. J., Zheng, X., & Coenen, F. (2009). Mining Allocating Patterns in Investment Portfolios. In Database Technologies (pp. 2657-2684). IGI Global. doi:10.4018/978-1-60566-058-5.ch159Mining Allocating Patterns in Investment Portfolios (Chapter)
Wang, Y. J., Zheng, X., & Coenen, F. (2009). Mining Allocating Patterns in Investment Portfolios. In Data Mining Applications for Empowering Knowledge Societies (pp. 110-135). IGI Global. doi:10.4018/978-1-59904-657-0.ch007Multi-Party Argument from Experience (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. P. (2009). Multi-Party Argument from Experience. In ARGMAS'09 (pp. 207-224). Budapest: AAMAS.PISA — Pooling Information from Several Agents: Multiplayer Argumentation from Experience (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2009). PISA — Pooling Information from Several Agents: Multiplayer Argumentation from Experience. In Unknown Conference (pp. 133-146). Springer London. doi:10.1007/978-1-84882-171-2_10The EMADS Extendible Multi-Agent Data Mining Framework (Chapter)
Albashiri, K. A., & Coenen, F. P. (2009). The EMADS Extendible Multi-Agent Data Mining Framework. In L. Cao (Ed.), Data Mining and Multiagent Integration (pp. 89-200). Berlin: Springer.2008
On extraction of Nutritional Patterns (NPS) using fuzzy association rule mining (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. (2008). On extraction of Nutritional Patterns (NPS) using fuzzy association rule mining. In HEALTHINF 2008 - 1st International Conference on Health Informatics, Proceedings Vol. 1 (pp. 34-42).Measuring and Explaining the Quality of Web Sites in the (Virtual) House of Representatives (Chapter)
Esterling, K. M., Lazer, D. M. J., & Neblo, M. A. (2007). Measuring and Explaining the Quality of Web Sites in the (Virtual) House of Representatives. In Current Issues and Trends in E-Government Research (pp. 146-162). IGI Global. doi:10.4018/978-1-59904-283-1.ch007Mining Allocating Patterns in One-Sum Weighted Items (Conference Paper)
Wang, Y. J., Zheng, X., Coenen, F., & Li, C. Y. (2008). Mining Allocating Patterns in One-Sum Weighted Items. In 2008 IEEE International Conference on Data Mining Workshops. IEEE. doi:10.1109/icdmw.2008.112A Weighted Utility Framework for Mining Association Rules (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. (2008). A Weighted Utility Framework for Mining Association Rules. In 2008 Second UKSIM European Symposium on Computer Modeling and Simulation. IEEE. doi:10.1109/ems.2008.73Document-Base Extraction for Single-Label Text Classification (Conference Paper)
Wang, Y. J., Sanderson, R., Coenen, F., & Leng, P. (n.d.). Document-Base Extraction for Single-Label Text Classification. In Unknown Conference (pp. 357-367). Springer Berlin Heidelberg. doi:10.1007/978-3-540-85836-2_34Mining Efficiently Significant Classification Association Rules (Journal article)
Wang, Y. J., Xin, Q., & Coenen, F. (2008). Mining Efficiently Significant Classification Association Rules. Unknown Journal, 443-467. doi:10.1007/978-3-540-78488-3_26Weighted Association Rule Mining from Binary and Fuzzy Data (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. (n.d.). Weighted Association Rule Mining from Binary and Fuzzy Data. In Unknown Conference (pp. 200-212). Springer Berlin Heidelberg. doi:10.1007/978-3-540-70720-2_16Agent Based Frequent Set Meta Mining: Introducing EMADS (Conference Paper)
Albashiri, K. A., Coenen, F., & Leng, P. (n.d.). Agent Based Frequent Set Meta Mining: Introducing EMADS. In Unknown Conference (pp. 23-32). Springer US. doi:10.1007/978-0-387-09695-7_3Mining Fuzzy Association Rules from Composite Items (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. (n.d.). Mining Fuzzy Association Rules from Composite Items. In Unknown Conference (pp. 67-76). Springer US. doi:10.1007/978-0-387-09695-7_7A Framework for Mining Fuzzy Association Rules from Composite Items", Algorithms for (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. P. (2008). A Framework for Mining Fuzzy Association Rules from Composite Items", Algorithms for. In ASLIP'08 (pp. 65-77). Osaka: PAKDD.Argument Based Moderation of Benefit Assessment (Conference Paper)
Wardeh, M., Bench-Capon, T. J. M., & Coenen, F. (2008). Argument Based Moderation of Benefit Assessment. In LEGAL KNOWLEDGE AND INFORMATION SYSTEMS Vol. 189 (pp. 128-137). doi:10.3233/978-1-58603-952-3-128Arguments from Experience: The PADUA Protocol (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2008). Arguments from Experience: The PADUA Protocol. In COMPUTATIONAL MODELS OF ARGUMENT, PROCEEDINGS OF COMMA 2008 Vol. 172 (pp. 405-416). Retrieved from https://www.webofscience.com/Efficiently Mining Significant Classification Association Rules (Chapter)
Wang, Y. J., Xin, Q., & Coenen, F. P. (2008). Efficiently Mining Significant Classification Association Rules. In T. Y. Lin, A. Wasilewska, F. Petry, & Y. Xie (Eds.), Data Mining: Foundations and Practice (pp. 443-468). Heidelberg: Springer.Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework (Conference Paper)
Khan, M. S., Muyeba, M., & Coenen, F. P. (2008). Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework. In ASLIP'08 (pp. 53-64). Osaka: PAKDD.Hybrid Rule Ordering in Classification Association Rule Mining (Journal article)
Wang, Y. J., Xin, Q., & Coenen, F. P. (2008). Hybrid Rule Ordering in Classification Association Rule Mining. Transactions on Machine Learning and Data Mining in Pattern Recognition, 1-16.Mining fuzzy association rules from composite items (Conference Paper)
Sulaiman Khan, M., Muyeba, M., & Coenen, F. (2008). Mining fuzzy association rules from composite items. In IFIP Advances in Information and Communication Technology Vol. 276 (pp. 67-76).Research and Development in Intelligent Systems XXV (Conference Paper)
Bramer, M., Coenen, F. P., & Petridis, M. (Eds.) (2008). Research and Development in Intelligent Systems XXV. In AI'2008 (pp. 300). Cambridge: Springer.The PADUA Protocol (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2008). The PADUA Protocol. In COMMA 2008 (pp. 405-416). Toulouse: IOS Press.2007
A Novel Rule Weighting Approach in Classification Association Rule Mining (Conference Paper)
Wang, Y. J., Xin, Q., & Coenen, F. (2007). A Novel Rule Weighting Approach in Classification Association Rule Mining. In Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007). IEEE. doi:10.1109/icdmw.2007.126The effect of threshold values on association rule based classification accuracy (Journal article)
Coenen, F., & Leng, P. (2007). The effect of threshold values on association rule based classification accuracy. Data & Knowledge Engineering, 60(2), 345-360. doi:10.1016/j.datak.2006.02.005'A Novel Rule Weighting Approach in Classification Association Rule Mining' (Conference Paper)
Wang, J., Xin, Q., & Coenen, F. P. (2007). 'A Novel Rule Weighting Approach in Classification Association Rule Mining'. In ICSM'07 Workshop (pp. x). Nebraska, USA: IEEE.'Association Rule Mining in The Wider Context of Text, Images and Graphs.' (Journal article)
Coenen, F. (2007). 'Association Rule Mining in The Wider Context of Text, Images and Graphs.'. Expert Upda, 9(3), 5-9.A Novel Rule Ordering Approach in Classification Association Rule Mining (Conference Paper)
Wang, Y. J., Xin, Q., & Coenen, F. (n.d.). A Novel Rule Ordering Approach in Classification Association Rule Mining. In Unknown Conference (pp. 339-348). Springer Berlin Heidelberg. doi:10.1007/978-3-540-73499-4_26An effective fuzzy health rule mining algorithm. (Conference Paper)
Khan, M. S., Muyeba, M., Tjortjis, C., & Coenen, F. P. (2007). An effective fuzzy health rule mining algorithm.. In Proc. 7th Annual Workshop on Computational Intelligence (pp. xx). Aberdeen: University of Aberdeen.Association Rule Mining in The Wider Context of Text, Images and Graphs. (Conference Paper)
Coenen, F. P. (2007). Association Rule Mining in The Wider Context of Text, Images and Graphs.. In UKKDD'7 (pp. 1-6). Canterbury: University of Kent.Dynamic Rule Mining for Argumentation Based Systems (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (n.d.). Dynamic Rule Mining for Argumentation Based Systems. In Unknown Conference (pp. 65-78). Springer London. doi:10.1007/978-1-84800-094-0_6Frequent Set Meta Mining: Towards Multi-Agent Data Mining (Conference Paper)
Albashiri, K. A., Coenen, F., Sanderson, R., & Leng, P. (n.d.). Frequent Set Meta Mining: Towards Multi-Agent Data Mining. In Unknown Conference (pp. 139-151). Springer London. doi:10.1007/978-1-84800-094-0_11PADUA protocol:TB Strategies and tactics (Conference Paper)
Wardeh, M., Bench-Capon, T., & Coenen, F. (2007). PADUA protocol:TB Strategies and tactics. In SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, PROCEEDINGS Vol. 4724 (pp. 465-+). Retrieved from https://www.webofscience.com/Research and Development in Intelligent Systems XXIV (Conference Paper)
Bramer, M., Coenen, F. P., & Petridis, M. (Eds.) (2007). Research and Development in Intelligent Systems XXIV. In AI'2007 (pp. 200). London: Springer.Statistical Identification of Key Phrases for Text Classification (Conference Paper)
Coenen, F., Leng, P., Sanderson, R., & Wang, Y. J. (n.d.). Statistical Identification of Key Phrases for Text Classification. In Unknown Conference (pp. 838-853). Springer Berlin Heidelberg. doi:10.1007/978-3-540-73499-4_63Text Classification using Language-independent Pre-processing (Conference Paper)
Wang, Y. J., Coenen, F., Leng, P., & Sanderson, R. (2007). Text Classification using Language-independent Pre-processing. In Unknown Conference (pp. 413-417). Springer London. doi:10.1007/978-1-84628-663-6_342006
Towards an Agent-Based Framework for Online After-Sale Services (Conference Paper)
Lu Zhang., Coenen, F., Huang, W., & Leng, P. (2006). Towards an Agent-Based Framework for Online After-Sale Services. In 2006 3rd International IEEE Conference Intelligent Systems. IEEE. doi:10.1109/is.2006.348456Tree-based partitioning of date for association rule mining (Journal article)
Ahmed, S., Coenen, F., & Leng, P. (2006). Tree-based partitioning of date for association rule mining. Knowledge and Information Systems, 10(3), 315-331. doi:10.1007/s10115-006-0010-1The Knowledge Bazaar (Journal article)
Craker, B., & Coenen, F. (2006). The Knowledge Bazaar. Knowledge-Based Systems, 19(5), 341-347. doi:10.1016/j.knosys.2005.11.019Partitioning strategies for distributed association rule mining (Journal article)
COENEN, F., & LENG, P. (2006). Partitioning strategies for distributed association rule mining. The Knowledge Engineering Review, 21(1), 25-47. doi:10.1017/s0269888906000786Improved Methods for Extracting Frequent Itemsets from Interim-Support Trees (Conference Paper)
Coenen, F., Leng, P., Pagourtzis, A., Rytter, W., & Souliou, D. (2006). Improved Methods for Extracting Frequent Itemsets from Interim-Support Trees. In Unknown Conference (pp. 263-276). Springer London. doi:10.1007/978-1-84628-226-3_20Research and Development in Intelligent Systems XXIII (Conference Paper)
Bramer, M., Coenen, F., & Tueson, A. (Eds.) (2006). Research and Development in Intelligent Systems XXIII. In AI'2006 (pp. 419). London: Springer..Towards an Agent-Based Framework for Online After-Sale Services. (Conference Paper)
Zhang, L., Coenen, F. P., Huang, W., & Leng, P. H. (2006). Towards an Agent-Based Framework for Online After-Sale Services.. In IEEE Conference On Intelligent Systems (pp. 06EX1304C). London: IEEE.2005
Obtaining Best Parameter Values for Accurate Classification (Conference Paper)
Coenen, F., & Leng, P. (n.d.). Obtaining Best Parameter Values for Accurate Classification. In Fifth IEEE International Conference on Data Mining (ICDM'05). IEEE. doi:10.1109/icdm.2005.105'Selection of Significant Rules in Classification Association Rule Mining' (Conference Paper)
Wang, Y. J., Qin, X., & Coenen, F. (2005). 'Selection of Significant Rules in Classification Association Rule Mining'. In ICDM 2005 (pp. 106-108). Houston: Saint Mary's University, Halifax, Canada.Proceedings 25th BCS-SGAI AI Conference: Research and Development in Intelligent Systems XXII (Conference Paper)
Bramer, M., Coenen, F., & Allen, T. (Eds.) (2005). Proceedings 25th BCS-SGAI AI Conference: Research and Development in Intelligent Systems XXII. In AI'2005 (pp. 9999). London: Springer.Proceedings of the first UK Knowledge Discovery in Data Symposium (Conference Paper)
Coenen, F. (Ed.) (2005). Proceedings of the first UK Knowledge Discovery in Data Symposium. In UKKDD'05 (pp. 9999). Liverpool: Dept of Computer Science, University of Liverpool.The Knowledge Bazaar (Conference Paper)
Craker, B., & Coenen, F. (n.d.). The Knowledge Bazaar. In Unknown Conference (pp. 37-49). Springer London. doi:10.1007/1-84628-224-1_4Threshold Tuning for Improved Classification Association Rule Mining (Conference Paper)
Coenen, F., Leng, P., & Zhang, L. (2005). Threshold Tuning for Improved Classification Association Rule Mining. In Unknown Conference (pp. 216-225). Springer Berlin Heidelberg. doi:10.1007/11430919_272004
An Evaluation of Approaches to Classification Rule Selection (Conference Paper)
Coenen, F., & Leng, P. (n.d.). An Evaluation of Approaches to Classification Rule Selection. In Fourth IEEE International Conference on Data Mining (ICDM'04). IEEE. doi:10.1109/icdm.2004.10012Data structure for association rule mining: T-trees and P-trees (Journal article)
Coenen, F., Leng, P., & Ahmed, S. (2004). Data structure for association rule mining: T-trees and P-trees. IEEE Transactions on Knowledge and Data Engineering, 16(6), 774-778. doi:10.1109/tkde.2004.8A Tree Partitioning Method for Memory Management in Association Rule Mining (Journal article)
Ahmed, S., Coenen, F., & Leng, P. (2004). A Tree Partitioning Method for Memory Management in Association Rule Mining. Unknown Journal, 331-340. doi:10.1007/978-3-540-30076-2_33Strategies for Partitioning Data in Association Rule Mining (Chapter)
Ahmed, S., Coenen, F., & Leng, P. (2004). Strategies for Partitioning Data in Association Rule Mining. In Research and Development in Intelligent Systems XX (pp. 127-139). Springer London. doi:10.1007/978-0-85729-412-8_10Tree Structures for Mining Association Rules (Journal article)
Coenen, F., Goulbourne, G., & Leng, P. (2004). Tree Structures for Mining Association Rules. Data Mining and Knowledge Discovery, 8(1), 25-51. doi:10.1023/b:dami.0000005257.93780.3bUsing Domain Knowledge to Boost Case-Based Diagnosis: An Experimental Study in a Domain with Very Poor Data Quality (Chapter)
Zhang, L., Coenen, F., & Leng, P. (2004). Using Domain Knowledge to Boost Case-Based Diagnosis: An Experimental Study in a Domain with Very Poor Data Quality. In Applications and Innovations in Intelligent Systems XI (pp. 137-151). Springer London. doi:10.1007/978-1-4471-0643-2_102003
T-trees, vertical partitioning and distributed association rule mining (Conference Paper)
Coenen, F., Leng, P., & Ahmed, S. (n.d.). T-trees, vertical partitioning and distributed association rule mining. In Third IEEE International Conference on Data Mining. IEEE Comput. Soc. doi:10.1109/icdm.2003.1250965Setting attribute weights for k-NN based binary classification via quadratic programming (Journal article)
Zhang, L., Coenen, F., & Leng, P. (2003). Setting attribute weights for k-NN based binary classification via quadratic programming. Intelligent Data Analysis, 7(5), 427-441. doi:10.3233/ida-2003-75042002
Formalising optimal feature weight setting in case based diagnosis as linear programming problems (Journal article)
Zhang, L., Coenen, F., & Leng, P. (2002). Formalising optimal feature weight setting in case based diagnosis as linear programming problems. Knowledge-Based Systems, 15(7), 391-398. doi:10.1016/s0950-7051(02)00023-05th European conference on principles of knowledge discovery in databases (Conference Paper)
COENEN, F. (2002). 5th European conference on principles of knowledge discovery in databases. In The Knowledge Engineering Review Vol. 17 (pp. 197-203). Cambridge University Press (CUP). doi:10.1017/s026988890200022xAn Experimental Study of Increasing Diversity for Case-Based Diagnosis (Conference Paper)
Zhang, L., Coenen, F., & Leng, P. (2002). An Experimental Study of Increasing Diversity for Case-Based Diagnosis. In Unknown Conference (pp. 448-459). Springer Berlin Heidelberg. doi:10.1007/3-540-46119-1_33Finding Association Rules with Some Very Frequent Attributes (Conference Paper)
Coenen, F., & Leng, P. (2002). Finding Association Rules with Some Very Frequent Attributes. In Unknown Conference (pp. 99-111). Springer Berlin Heidelberg. doi:10.1007/3-540-45681-3_9Optimising Association Rule Algorithms Using Itemset Ordering (Chapter)
Coenen, F., & Leng, P. (2002). Optimising Association Rule Algorithms Using Itemset Ordering. In Research and Development in Intelligent Systems XVIII (pp. 53-66). Springer London. doi:10.1007/978-1-4471-0119-2_52001
Toward logical analysis of tabular rule-based systems (Journal article)
Lig?za, A. (2001). Toward logical analysis of tabular rule-based systems. International Journal of Intelligent Systems, 16(3), 333-360. doi:3.0.co;2-r">10.1002/1098-111x(200103)16:3<333::aid-int1011>3.0.co;2-rDOI: 10.1002/1098-111x(200103)16:3<333::aid-int1011>3.0.co;2-r
An Architecture For Web-based Post-sales Service In A Flexible Manufacturing Environment (Conference Paper)
Zhang, W., Coenen, F., & Leng, P. (n.d.). An Architecture For Web-based Post-sales Service In A Flexible Manufacturing Environment. In Unknown Conference (pp. 407-416). Kluwer Academic Publishers. doi:10.1007/0-306-47009-8_29Computing Association Rules Using Partial Totals (Conference Paper)
Coenen, F., Goulbourne, G., & Leng, P. (2001). Computing Association Rules Using Partial Totals. In Unknown Conference (pp. 54-66). Springer Berlin Heidelberg. doi:10.1007/3-540-44794-6_5Towards Integrated Online Support for Field Service Engineers in a Flexible Manufacturing Context (Chapter)
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