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COMP513 Data Mining

Updated: 18 January 2010
Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study Online Level
Armidale Semester 1 Off Campus D - Comp/internet essential
Armidale Semester 1 On Campus D - Comp/internet essential
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
Co-requisites None
Restrictions COMP313
Notes

COMP389 or COMP589 desirable
Combined Units COMP313 - Data Mining
Coordinator(s) Paul Kwan (wkwan2@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
Text refers to: Semester 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%
Assessment Notes
Short answers.
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1 GA: 1, 6
Project 15%
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 2, 3 GA: 1, 6
Report 20% 3000
Assessment Notes
Research report.
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 4 GA: 1, 2, 4
Final Examination 2 hrs 55%
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1 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. program selected algorithms for data mining applications;
  3. evaluate the effectiveness and the performance of data mining algorithms;
  4. select appropriate algorithms for real data mining applications;
  5. make use of research papers to gain a deeper understanding of a particular area of data mining;
  6. write a report on a particular area of data mining.

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
Assignment is a Literature Review. 		True True
4 Information Literacy
Many aspects of IC involved including web usage. 		True True
6 Problem Solving
Algorithm performance and application. 		True True True
   

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