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 M Gairing
Computer Science
M.Gairing@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2019-20 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

(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

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.

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

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

   10

       40
Timetable (if known)              
Private Study 110
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Final Exam There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :May  150 minutes.    75       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
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 semester 2  18 hours    12       
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 semester 2  2.5 hours    13       

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