Monash University

Leader(s): Grace Rumantir

Offered

Caulfield First semester 2009 (Day)

Synopsis

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, Machine learning, Genetic Algorithms and Fuzzy Systems. Students are able to choose an appropriate technique to suit a particular situation.

Objectives

At the completion of this unit students should have developed a knowledge of:

1. the techniques and methods for data exploration in large databases, both those currently being used and those which are presently being researched;
2. become familiar with the currently available techniques for the extraction of information from large databases;
3. the purpose of data mining.
4. the major techniques for data mining.
5. to allow them to apply a process to the acquisition of knowledge from a data store.

At the completion of this unit students will have developed attitudes to enable them to:

1. Appreciate the potential for data mining techniques to permit access to private information and understand this must be done only in the proper context.
2. Practise ethical behaviour when when conducting data mining exercises.

At the completion of this unit students will have the skills to:

1. choose an appropriate technique for a particular situation;
2. use a number of implementations of data mining software.

Assessment

Practical Assignments: 40%
Examination: 60%

Contact hours

One x 2hr lecture/week, one x 2hr laboratory/week

Prerequisites

FIT1004 or CSE2132 or equivalent

Prohibitions

CSE3212 (Translation for CSE3212)

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu.au/units/fit3073/