Module Details
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title DATA MINING AND VISUALISATION
Code COMP527
Coordinator Dr D Bollegala
Computer Science
Danushka.Bollegala@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2016-17 Level 7 FHEQ Second Semester 15

Aims

To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining.


Learning Outcomes
A critical awareness of current problems and research issues in Data Mining.

A comprehensive understanding of current advanced scholarship and research in data mining and how this may contribute to the effective design and implementation of data mining applications.
The ability to consistently apply knowledge concerning current data mining research issues in an original manner and produce work which is at the forefront of current developments in the sub-discipline of data mining.
A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining.

Syllabus
1 
  1. Introduction to Data Mining, Text Mining, Data Warehousing, scope and challenges. 
  2. Classification, problem definition, basic approaches (rules, trees).
  3. Advanced solutions to the challenges of classification and regression, evaluation possibilities for classification algorithms.
  4. Input preprocessing and hybrid solutions to data mining challenges.
  5. Association Rule Mining (ARM), problem definition, current challenges and solutions.
  6. Clustering, problem definiti on, challenges, basic and advanced solutions.
  7. Visualisation methods and their application to data mining will be studied using several freely available visualisation tools.
  8. Web mining and information retrieval systems. Learning ranking functions.
  9. Sequntial/temporal data mining algorithms.
  10. Large scale data mining approaches and distributed learning algorithms.

Teaching and Learning Strategies

Lecture -

http://cgi.csc.liv.ac.uk/~danushka/datamining.html

Tutorial -

http://cgi.csc.liv.ac.uk/~danushka/datamining.html

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 30

   10

       40
Timetable (if known) http://cgi.csc.liv.ac.uk/~danushka/datamining.html
  		  http://cgi.csc.liv.ac.uk/~danushka/datamining.html
  		       
Private Study 110
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Unseen Written Exam  150  May  75  Yes  Standard UoL penalty applies  Final exam Notes (applying to all assessments) Two programming assignments (25% marks) and a final written examination (75% marks).  
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Coursework  18 hours   2  12  Yes  Standard UoL penalty applies  Assessment 1 
Coursework  2.5 hours  2  13  Yes  Standard UoL penalty applies  Assessment 2 

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
Explanation of Reading List: