COMP8400 Algorithms and Techniques for Data Mining

Offered By	Department of Computer Science
Academic Career	Graduate Coursework
Course Subject	Computer Science
Offered in	First Semester, 2009 and First Semester, 2010
Unit Value	6 units
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.
Learning Outcomes	Students participating this course will learn about: - the data mining process and important issues around data cleaning, pre-processing and integration; - the main concepts of data warehousing; - the principle algorithms and techniques used in data mining, such as clustering, association mining, classification and prediction; - the various application and current research areas in data mining, such as Web and text mining, stream data mining; - ethical and social impacts of data mining. Practical lab sessions using a state-of-the-art open source data mining tool will allow students to gain expertise in 'hands on data' mining, while tutorial sessions covering overview research papers will highlight important data mining issues in more depth.
Indicative Assessment	Assignments (30%); Presentation (20%); Final Examination (50%)
Workload	One two-hour lecture per week, four or five laboratories and four or five tutorials
Course Classification(s)	AdvancedAdvanced courses are designed for students having reached 'first degree' level of assumed knowledge, which provide a deep understanding of contemporary issues; or 'second degree' and higher levels of knowledge; or for transition to research training programs.
Areas of Interest	Computer Science
Eligibility	An undergraduate degree. A degree in the sciences or engineering would be an advantage.
Assumed Knowledge and Required Skills	Assumed knowledge is equivalent to having studied at least an introductory database course and intermediate programming and data structure courses.
Requisite Statement	Enrolment in Master of Computing
Prescribed Texts	"Data Mining - Concepts and Techniques", Jaiwei Han and Micheline Kamber, Morgan Kaufmann.
Preliminary Reading	http://cs.anu.edu.au/student/comp8400/links.php
Other Information	This course can be studied for credit in the following programs: Master of Computing/Master of Comuting Honours Graduate Studies and as an elective in other programs.

The information published on the Study at ANU 2009 website applies to the 2009 academic year only. All information provided on this website replaces the information contained in the Study at ANU 2008 website.