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Year:

COMP513 Data Mining

Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study
Armidale Semester 1 On line
Armidale Semester 1 On Campus
Melb Institute of Technology MIT-Sydney Trimester 1 On Campus
Online level
  • Level D - Comp/internet essential
  • Level E - Wholly online unit
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
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.

Prescribed Material
Mandatory

Text(s):

Note: Students are expected to purchase prescribed material

Data Mining: Concepts and Techniques
ISBN: 9781558609013
Han, J. and Kamber, M., Morgan Kaufmann 2nd ed. 2006
Recommended Material
Optional
None
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
   

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