COMP513 Data Mining
Updated: 26 May 2009| 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 award or Honours | ||||||||||||||||||
| Co-requisites | None | ||||||||||||||||||
| Restrictions | COMP313 | ||||||||||||||||||
| 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 COMP130 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 |
COMP313 - 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(s):
Note: Students are expected to purchase prescribed material
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| Disclaimer | Unit information may be subject to change prior to commencement of the teaching period. |