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
Upon successful completion, students will have the knowledge and skills to:
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.
Other Information
This course can be studied for credit in the following programs:
Master of Computing/Master of Computing Honours
Graduate Studies
and as an elective in other programs.
Indicative Assessment
Two assignments (18% each); Paper presentation and report (14%); Final examination (50%)
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
One two-hour lecture per week, four laboratories and four or five tutorials
Requisite and Incompatibility
Prescribed Texts
Han, Kamber and Pei: Data Mining - Concepts and Techniques, 3rd edition, 2011.
Preliminary Reading
http://cs.anu.edu.au/courses/COMP8400/
Assumed Knowledge
Assumed knowledge is equivalent to having studied at least an introductory database course and intermediate programming and data structure courses.
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2016 | $3480 |
- International fee paying students
| Year | Fee |
|---|---|
| 2016 | $4638 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 2824 | 15 Feb 2016 | 26 Feb 2016 | 31 Mar 2016 | 27 May 2016 | In Person | N/A |
