Massive amounts of data are collected by public and private organisations, while the Web offers rich information about almost every aspect of human life and society. Analysing such data can provide significant benefits to an organisation or research project. This course provides a practical focus on the technology and research in the area of data mining. It focuses on the algorithms and techniques and less on the mathematical and statistical foundations.
Additional source review: awaiting review. Published wording is shown below.
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Requisite and Incompatibility
To enrol in this course you must have completed COMP7240 or COMP6240 or COMP2400; and COMP6730 or COMP7230 or COMP6710. Incompatible with COMP3420 and COMP3425 and COMP8400 and COMP8910.
Referenced entries (10)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP7240 · 2027 · saved source not linked
- COMP6240 · 2027
- COMP2400 · 2027 · saved source not linked
- COMP6730 · 2027
- COMP7230 · 2027 · saved source not linked
- COMP6710 · 2027
- COMP3420 · 2027 · saved source not linked
- COMP3425 · 2027 · saved source not linked
- COMP8400 · 2027 · saved source not linked
- COMP8910 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Critically analyse and justify the steps involved in the data mining process,
Anticipate and identify data issues related to data mining,
Research, test and apply the principal algorithms and techniques used in data mining,
Justify suitable techniques to use for a given data mining problem,
Appraise and reflect upon the results of a data mining project using suitable measurements,
Investigate application areas and current research directions of data mining,
Reflect upon ethical and social impacts of data mining.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | First Semester | 5401 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 6677 | 21 Feb 2028 | 28 Feb 2028 | 31 Mar 2028 | 26 May 2028 | In Person | N/A |
Complete offering text
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. The list of offerings for future years is indicative only. Class summaries, if available, can be accessed by clicking on the View link for the relevant class number. 2027 2028 First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 5401 22 Feb 2027 01 Mar 2027 31 Mar 2027 28 May 2027 In Person N/A First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 6677 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
Source evidence (1 saved copy)
Matching copies share this record. Their original file locations and checksums are retained.
assets/Courses/Data Science/8000/Data Mining/Data Mining - ANU.html
Published ANU page · saved page URL
SHA-256: be56435448edfa3015d36e218aaf4b6748ad866f8f9522642834eefd58d435aa