COMP313 Data Mining
Updated: 15 January 2013| 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 | None | |||||||||
| Co-requisites | None | |||||||||
| Restrictions | COMP513 | |||||||||
| Notes | COMP389 desirable |
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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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| Important Information |
Where calculators are permitted in examinations, it must be selected from an approved list, which can be accessed from the Further Information link below. |
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| Prescribed Material Mandatory |
Text(s):
Note: Students are expected to purchase prescribed material. Please note that textbook requirements may vary from one teaching period to the next.
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| Disclaimer | Unit information may be subject to change prior to commencement of the teaching period. |
| Assessment |
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| Learning Outcomes (LO) |
Upon completion of this unit, students will be able to:
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| Graduate Attributes (GA) |
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