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COMP313 Data Mining

Updated: 12 October 2011
Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study
Armidale Trimester 1 Off Campus
Armidale Trimester 1 On Campus
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
Combined Units COMP513 - 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: Methods and Techniques
ISBN: 9780170136761
Shawkat Ali, A.B.M, and Wasimi, S.A., Thompson 2007
Text refers to: Trimester 1 , On and Off Campus
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
Assessment
Title Exam Length Weight Mode No. Words
Assignment 1 10% On/Off Campus
Assessment Notes
Computational assignment. Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2,3 GA: 1, 6
Assignment 2 10% On/Off Campus
Assessment Notes
Computational assignment. Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2,3 GA: 1, 6
Assignment 3 15% On/Off Campus
Assessment Notes
Computational assignment. Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2,3 GA: 1, 6
Final Examination 2 hrs 65% On/Off Campus
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 2, 3 GA: 1, 6

Learning Outcomes (LO) Upon completion of this unit, students will be able to:
  1. explain the main algorithms used in data mining;
  2. select appropriate algorithms for data mining applications; and
  3. evaluate the effectiveness and the performance of data mining algorithms.

Graduate Attributes (GA)
Attribute Taught Assessed Practised
1 Knowledge of a Discipline
Knowledge of data mining algorithms is developed through lectures and in the assessment tasks. 		True True True
2 Communication Skills
Assignments require composition and interpretation of results from data mining exercises. 		True True
4 Information Literacy
Many aspects of IC involved including web usage. The unit will use the WEKA data mining package which runs on a computer. Assignments are submitted electronically. Course materials are accessed from a web site. Practical classes are in computer labs. 		True True True
6 Problem Solving
Students are required to solve data mining tasks by use of appropriate algorithms. 		True True True
   

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