COMP313 Data Mining
Updated: 12 November 2008| Credit Points | 6 | ||||||||||||||||||
| Offering |
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| Online level |
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| Intensive School(s) | None | ||||||||||||||||||
| Supervised Exam |
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| Pre-requisites | None | ||||||||||||||||||
| Co-requisites | None | ||||||||||||||||||
| Restrictions | COMP513 | ||||||||||||||||||
| Notes | COMP389 or COMP589 desirable; on-campus online D; off-campus online E; 200- and 300-level COMP units (excluding COMP286) require a knowledge of, and programming experience with the C or C++ language. Any student who completed COMP 130 prior to 1995 should contact the School of Science and Technology for advice. It is recommended that students enrolled for 200-level and above COMP units have access to an IBM compatible computer running the Linux Operating System. |
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| Combined Units |
COMP513 - Data Mining |
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| Coordinator(s) | Neil Dunstan (neil@turing.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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| Prescribed Material Mandatory |
Text information will be published prior to commencement of the teaching period. | ||||||||||||||||||
| Recommended Material Optional |
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. |