An EXAM length 2 HOURS weighted at 50%. An ASSIGMT weighted at 50% (3000 words).

Exam 50% length 2 hours (Learning outcomes 2 and 3)
Assignment 50% (Learning outcomes 1 and 4)

Knowledge discovery and data mining in context.
Data mining primitives, languages and system architecture.
Data mining methodology and algorithms.
Text mining algorithms and Information extraction systems.
Data mining and data privacy.
Social impact and ethical issues

24 hours of lectures
26 hours of practical/tutorials

Data Mining software: MATLAB, WEKA

K. Cios, W. Pedrycz, R. Swiniarski, L. Kurgan, Data Mining: A Knowledge Discovery Approach, Springer, ISBN: 978-0-387-33333-5, 2007.
Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 2nd edition, Morgan Kaufmann, ISBN 1558609016, 2006.
Glenn J. Myatt, Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, John Wiley, ISBN: 0-470-07471-X, November 2006.
Olivia Parr Rud, Data Mining Cookbook, modeling data for marketing, risk, and CRM. Wiley, 2001.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Addison Wesley (May, 2005). ISBN: 0321321367

Prior study of Statistics and AI or equivalent

1) TO GAIN A THOROUGH UNDERSTANDING OF THE FIELD OF KNOWLEDGE DISCOVERY, DATA MINING CONCEPTS AND METHODOLOGIES, IN ORDER TO CRTICALLY IDENTIFY THE MOST SUITABLE DATA MODELLING FOR SOLVING SPECIFIC PROBLEMS IN THE CONTEXT OF KNOWLEDGE MANAGEMENT. (Knowledge and Understanding).
2) TO CRTICALLY INVESTIGATE AND IDENTIFY THE BUSINESS REQUIREMENTS AND DEVELOP A MODEL FOR DATA MINING APPLICATION. (Application).
3) TO SELECT THE MOST SUITABLE DATA MINING TECHNIQUES FOR SOLVING SPECIFIC PROBLEMS AND TO CRITICALLY EVALUATE THEIR STRENGTHS AND LIMITATIONS. (Problem Solving).
4) TO CRITICALLY ANALYSE THE SOCIETAL IMPACTS AND ETHICAL ISSUES ASSOCIATED WITH DATA MINING. (Analysis).