FIT5045 - Knowledge discovery and data mining

Leader(s): Dr Grace Rumantir

Offered

Caulfield Second semester 2009 (Day)

Synopsis

Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.

Objectives

At the completion of this unit students will:

1. Be able to differentiate between supervised and unsupervised learning;
2. Know how to apply the main techniques for supervised and unsupervised learning;
3. Know how to use statistical methods for evaluating data mining models;
4. Be able to perform data pre-processing for data with outliers, incomplete and noisy data;
5. Be able to extract and analyse patterns from data using a data mining tool.

Assessment

Unit Test: 20%; Assignment: 20%; Examination (3 hours): 60%

Contact hours

4 hrs/week

Prerequisites

For MAIT students, FIT9017, FIT9018, FIT9019, FIT9030, FIT9020 and FIT4037.

+ Sound fundamental knowledge in maths and statistics
+ Basic database and computer programming knowledge

Prohibitions

CSE5230, FIT5024

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

http://www.infotech.monash.edu.au/units/fit5045/