Module Descriptors
KNOWLEDGE DISCOVERY AND DATA MINING (EFREI)
COSE70488
Key Facts
School of Digital, Technologies and Arts
Level 7
15 credits
Contact
Leader: Euan Wilson
Email: E.D.Wilson@staffs.ac.uk
Hours of Study
Scheduled Learning and Teaching Activities: 36
Independent Study Hours: 114
Total Learning Hours: 150
Assessment
- EXAMINATION - UNSEEN IN EXAMINATION CONDITIONS weighted at 50%
- ASSIGNMENT weighted at 50%
Module Details
Module Texts
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
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
Module Resources
Data Mining software: MATLAB, WEKA
Module Special Admissions Requirements
Prior study of Statistics and AI or equivalent
Module Learning Strategies
24 hours of lectures
26 hours of practical/tutorials
26 hours of practical/tutorials
Module Indicative Content
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
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
Module Additional Assessment Details
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)
Exam 50% length 2 hours (Learning outcomes 2 and 3)
Assignment 50% (Learning outcomes 1 and 4)