Created as part of the Master of information Technology and Master of Applied Information Technology Units - 2009

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

07/12/2017 - Unit disestablished at FEC 5/17 Item 7.3.

This is one of four core units in the Intelligence Systems Specialisation of the Master of Information Technology (MIT) and Master of Applied Information Technology (MAIT). It is also available as an elective in all FIT Masters.

At the completion of this unit students will:

• be able to differentiate between supervised and unsupervised learning;
• know how to apply the main techniques for supervised and unsupervised learning;
• know how to use statistical methods for evaluating data mining models;
• be able to perform data pre-processing for data with outliers, incomplete and noisy data;
• be able to extract and analyse patterns from data using a data mining tool;
• have an understanding of the difference between discovery of hidden patterns and simple query extractions in a dataset;
• have an understanding of the different methods available to facilitate discovery of hidden patterns in a dataset;
• have developed the ability to pre-process data in preparation for data mining experiments;
• have developed the ability to evaluate the quality of data mining models;
• be able to appreciate the need to have representative sample input data to enable learning of patterns embedded in population data;
• be able to appreciate the need to provide quality input data to produce useful data mining models;
• have acquired the skill to use the common features in data mining tools;
• have acquired the skill to use the visualisation features in a data mining tools to facilitate knowledge discovery from a data set;
• have acquired the skill to compare data mining models based on the results on a set of performance criteria;
• be able to work in a team to extract knowledge from a common data set using different data mining methods and techniques.

020199

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.

On-campus

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

6

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Study schedule for off-campus students:

(c.) Additional requirements (all students):

FIT

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

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

Grace Rumantir