| UofA | Computing Science | 2002-3 |
OBJECTIVE/DESCRIPTION:
Data Mining and Knowledge Discovery has become an active area of research,
attracting people from several disciplines including: database systems,
statistics, information retrieval, pattern recognition, AI/machine
learning, and data visualization.
The course will introduce data mining and data warehousing, and study
their principles, algorithms, implementations, and applications.
TOPICS:
The course will cover the following topics:
- An introduction to data mining and data warehousing: motivation and
applications.
- Basic data warehousing technology: data cube methods, data warehouse
construction and maintenance.
- Basic data mining techniques: characterization, association,
classificiation, clustering, and similarity-based mining.
- Advanced data mining applications: mining relational and transaction
data, mining time-related data, spatial data mining, textual data
mining, multimedia data mining, visual data mining, and Web mining.
GRADING:
Assignments (4x5%), Midterm exam (25%), Class presentation (16%), Project or
research report (39%).
TEXTBOOKS:
REFERENCES:
- Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad,
Gregory Piatetsky-Shapiro, Padhraic Smyth, , AAAI/MIT Press, 1997.
- Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. J.
Frawley, AAAI/MIT Press, 1991.
- Principles of Data Mining Course, David Hand, Heikki Mannila,
Padhraic Smyth, MIT Press, 2001
- Data Mining: Introductory and Advanced Topics, Margaret
H. Dunham, Prentice Hall, 2003
- Dealing with the data flood: Mining data, text and multimedia,
Edited by Jeroen Meij, SST Publications, 2002
PREREQUISITES:
An introductory course on Database Systems (CMPUT 391 or equivalent).
Preferred (but not required): CMPUT-366 (An Introduction to Artificial
Intelligence) and other courses on Database Systems, Machine Learning,
Information Retrieval, and Statistics.
Distributed: September 5, 2004