Module Specification

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.
1. Module Title Data Mining and Visualisation
2. Module Code COMP527
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester Second Semester
7. CATS Level Level 7 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr P Sen Computer Science Procheta.Sen@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 30

  10

      40
17.

Private Study

110
18.

TOTAL HOURS

150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

COMP516 Research Methods in Computer Science
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. 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.

 
28. Learning Outcomes
 

(LO1) A critical awareness of current problems and research issues in Data Mining.

 

(LO2) 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.

 

(LO3) 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.

 

(LO4) A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining.

 

(S1) Critical thinking and problem solving - Problem identification

 

(S2) Critical thinking and problem solving - Critical analysis

 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided
Notes: http://cgi.csc.liv.ac.uk/~danushka/datamining.html

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Not yet decided
Notes: http://cgi.csc.liv.ac.uk/~danushka/datamining.html

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: On-campus synchronous sessions

 
30. Syllabus
   

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

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

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (527) Final Exam There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :May 150 70
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (527.1) Programming Assignment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 6 of semes 0 15
  (527.2) Programming Assignment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 4 of semes 0 15