| UofA | Computing Science | 99-3 |
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:
Homeworks (10%), Midterm exam (30%), Class presentation (25%), Project or
research report (35%).
TEXTBOOKS:
- Data Mining: Concepts and Techniques, Jiawei Han and Micheline
Kamber, The Morgan Kaufmann Pub., 2000.
- Some recent conference/journal paper collection, , (class
distribution), 1999.
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.
- OLAP Solutions:Building Multidimensional Information, E. Thomsen, John
Wiley, 1997
- Readings in Agents, Michael N. Huhns and Munindar P. Singh, Morgan
Kaufmann Pub., 1998.
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 10, 1999