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Year:

COMP513 Data Mining

Updated: 29 January 2013
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 candidature in a postgraduate award
Co-requisites None
Restrictions COMP313
Notes

COMP389 or COMP589 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.

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.

Further information

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.

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%
Assessment Notes
Computational assignments - Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2-3 GA: 1, 6
Assignment 2 10%
Assessment Notes
Computational assignments - Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2-3 GA: 1, 6
Assignment 3 15%
Assessment Notes
Computational assignments - Short answers
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 2-4 GA: 1, 6
Final Examination 2 hrs 65%
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1-4 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 real data mining applications;
  3. evaluate the effectiveness and the performance of data mining algorithms; and
  4. demonstrate a high level of knowledge, interpretation, understanding, communication and computational skills.

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
Students are required to solve data mining tasks by use of appropriate algorithms.
True True True
   

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