Monash University Handbook 2010 Undergraduate - Unit

Synopsis

In the modern corporate world, data is viewed not only as a necessity for day-to-day operation, it is seen as a critical asset for decision making. However, raw data is of low value. Succinct generalisations are required before data gains high value. Data mining produces knowledge from data, making feasible sophisticated data-driven decision making. This unit will provide students with an understanding of the major components of the data mining process, the various methods and operations for data mining, knowledge of the applications and technical aspects of data mining, and an understanding of the major research issues in this area.

Objectives

At the completion of this unit students will have -
A knowledge and understanding of:

• the motivation and the need for data mining;
• characteristics of major components of the data mining process;
• the basic principles of methods and operations for data mining;
• case studies to bridge the connection between hands-on experience and real-world applications;
• key and emerging application areas;
• current major research issues.

• use data mining tools to solve data mining problems.

Assessment

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

Chief examiner(s)

Associate Professor Kai Ming Ting

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Prerequisites

FIT1004 or equivalent

Prohibitions

CSE3212, GCO3828

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

http://www.infotech.monash.edu.au/units/fit3002