COMP513 Data Mining
Updated: 19 April 2007| Credit Points | 6 | ||||||||||||
| Offering |
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| Online level |
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| Intensive School(s) | None | ||||||||||||
| Supervised Exam | There is a UNE Supervised Examination held at the end of the teaching period in which you are enrolled. | ||||||||||||
| Pre-requisites | candidature in a postgraduate degree or Honours | ||||||||||||
| Co-requisites | None | ||||||||||||
| Restrictions | None | ||||||||||||
| Notes | on-campus online D; off-campus online E; see COMP 280; COMP 389 or 589 desirable | ||||||||||||
| Combined Units | None | ||||||||||||
| Coordinator(s) | Xiaodi Huang (xhuang@une.edu.au) | ||||||||||||
| Unit Description |
Two lectures and a two-hour laboratory session per week. The topic, data mining, which is also known as Knowledge Discovery in Databases (KDD), examines large data sets for latent information which may be of commercial or scientific value. This unit focuses on algorithms for discovering patterns, associations and structures in data sets. Topics include affinity grouping, clustering, classification and online analytical processing. |
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| Prescribed Material Mandatory |
Textbook information is only available from 2008 units onwards. | ||||||||||||
| Recommended Material Optional |
Textbook information is only available from 2008 units onwards. | ||||||||||||
| Disclaimer | Unit information may be subject to change prior to commencement of the teaching period. |