COMP513 Data Mining
| 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 | |||||||||||||
| Restrictions | COMP 313 | ||||||||||||
| Notes | on-campus online D; off-campus online E; see COMP 280; COMP 389 or 589 desirable | ||||||||||||
| 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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| Recommended Material Optional |
None | ||||||||||||
| Disclaimer | Unit information may be subject to change prior to commencement of the teaching period. |
