Data mining is a natural extension of information technology skills for students who wish to build their expertise in the data storage and retrieval areas. It has increasing relevance and application in a broad range of domains in both government and commercial enterprises. It would enhance the employment prospects of students in a large number of potential employment categories in the IT industry.

This unit has been updated as part of a 2009 review and comparison of data with current Handbook, Syllabus + and Callista data.

30/11/2017 - Unit disestablished at FEC 5/17 Item 7.3.

This is a third level unit in the Bachelor of Information Technology and Systems (BITS) major in Systems Development. It has similarities to FIT3002, but concentrates on techniques and methods for data exploration. Data mining has increasing relevance to industry, and students undertaking a Systems Development major may require skills in this area.

At the completion of this unit students will:

• develop student knowledge of the techniques and methods for data exploration in large databases, both those currently being used and those which are presently being researched;
• be familiar with the currently available techniques for the extraction of information from large databases;
• have an understanding of the purpose of data mining;
• have an understanding of the major techniques for data mining;
• have developed the knowledge to allow them to apply a process to the acquisition of knowledge from a data store;
• appreciate the potential for data mining techniques to permit access to private information and understand this must be done only in the proper context;
• practice ethical behaviour when conducting data mining exercises;
• have developed the skill to choose an appropriate technique for a particular situation;
• have the skills to use a number of implementations of data mining software.

020303

This unit provides an overview of the techniques used to search for knowledge within a data set using both supervised and unsupervised learning. The techniques include Classification, Prediction, Clustering, Association discovery, Time sequence discovery, Sequential pattern discovery, Visualization, Statistical Methods, Decision Trees, Rule based methods, Neural networks, and Machine learning. Students are able to choose an appropriate technique to suit a particular situation.

On-campus

Examination (3 hours): 60%; In-semester assessment: 40%

2 hrs lectures/wk, 2 hrs laboratories/wk

FIT

FIT1004 or CSE2132 or equivalent

Strong foundation in high school mathematics and statistics.

Grace Rumantir