Monash University Handbook 2010 Undergraduate - Unit

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 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 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.

Assessment

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

Chief examiner(s)

Dr Grace Rumantir

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Prerequisites

FIT1004 or CSE2132 or equivalent

Prohibitions

CSE3212

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

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