Algorithms and Techniques for Data Mining COMP8400

Course overview

Assumed knowledge & required skills

Assumed knowledge is equivalent to having studied at least an introductory database course and intermediate programming and data structure courses.

Course description

Large amounts of data are increasingly being collected by public and private organisations, and research projects. Additionally, the Internet provides a very large source of information about almost every aspect of human life and society.

This course provided a practical focus on the technology and research in the area. It focuses on the algorithms and techniques and less on the mathematical and statistical foundations.

Course content

This course introduces students to the concepts, algorithms and techniques used in data mining. The topics covered will include data warehousing, data pre-processing and integration, the data mining process, data mining algorithms and techniques, data mining applications, as well as social and security aspects related to data mining.

The activities in the course will be some combination of lectures, tutorials and practical labs, reading of research papers, as well as smaller project works, as appropriate to the topic.

In the lab sessions we will be using open source data mining tools such as Rattle (developed by Graham Williams at ATO in Canberra) and possibly Weka.

Textbooks

Han, Jaiwei & Kamber, Micheline Data Mining - Concepts and Techniques, 2nd edition, 2006.

Workload

One two-hour lecture per week, four laboratories and four or five tutorials