Year:
COMP513 Data Mining
Updated: 02 November 2009| Credit Points | 6 | ||||||||||||||||||||||||
| Offering |
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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 award | ||||||||||||||||||||||||
| Co-requisites | None | ||||||||||||||||||||||||
| Restrictions | COMP313 | ||||||||||||||||||||||||
| Notes | COMP389 or COMP589 desirable |
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| Combined Units |
COMP313 - Data Mining |
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| Coordinator(s) | Paul Kwan (wkwan2@une.edu.au) | ||||||||||||||||||||||||
| Unit Description |
With the unprecedented rate at which data is being collected today, there is an emerging economic and scientific need to extract useful information from the data. Data mining is the process of automatic discovery of patterns in large data sets. This unit will provide an introduction to main topics in data mining and knowledge discovery, including association rules, classification, clustering, and online analytical processing. Emphasis will be placed on the algorithmic and systems issues, as well as the application of mining in real-world problems. |
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| Materials | Text information will be published prior to commencement of the teaching period. | ||||||||||||||||||||||||
| Disclaimer | Offer of some subjects is subject to viability. Information in these unit descriptions is subject to change prior to commencement of semester. |
