Introduced as a Tier-2 unit (advanced unit) for Master of Information Technology - 2014

03 Jun 2013 - This unit serves as a Tier-2 (advanced) unit in the new Master of Information Technology course - 2014.

24 July 2013 - Changing date of introduction from Semester 1, 2015 to Semester 2, 2014 as unit is required for MIT transition purposes (replacing FIT5045 for Intelligence Systems specialisation students in 2014).

9/9/13 - Modified Unit Objectives to comply with AQF requirements.

April 2017. On behalf of CE, adding Reasons for Change, and submitting on CE behalf. Updated exam duration from 3 to 2 hours as required by new University Examination procedures, effective S2 2017.

26/6/2018 - Updated exam period from 2 hours with 30 minutes reading/noting time back to 3 hours. This unit is to allow more comprehensive examination of students' command of knowledge at the analysing, synthesising and evaluating levels of the Bloom's Taxonomy.

29/06/2018: Admin - The assessment change to a 3 hour exam was not approved by the DD(E), therefore the assessment has been changed to a 2 hour exam with the standard 10 minutes reading and noting time.

5/10/2018: Update to prerequisites to allow Software engineering students to enrol as an approved elective. Effective 2019.

24/9/2019: Admin - adding 10 minutes to exam duration as per University requirements.

19/11/2020: Admin - this unit was disestablished at FEC 5/20 on 12/11/2020 as it is no longer required.

This is one of the advanced units (Tier-2) in the Master of Information Technology - 2014. The unit is also available as an elective for all FIT master.

For students enrolled in the Master of Information Technology (MIT) and Master of Applied Information Technology (MAIT) degree programs prior to 2014, the unit can serve as one of the core units in the Intelligence Systems Specialisation.

On successful completion of this unit students should be able to:

• explain the kinds of data from which knowledge can be mined, the way each data type can be presented to a data mining algorithm, the kinds of patterns that can be mined from each data type;
• evaluate the quality of data mining models;
• perform pre-processing of large-scale multi-dimensional datasets in preparation for data mining experiments;
• perform data pre-processing for data with outliers, incomplete and noisy data;
• compare the various learning algorithms and the ability to effectively apply suitable algorithms to mine frequent patterns and associations from data, to perform data classification, data clustering and regression analysis;
• use modern data mining tools to solve non-trivial data mining problems;
• research the current trends in data mining applications;
• work in a team to extract knowledge from a common dataset using various data mining methods and techniques.

020199

Advanced methods of discovering patterns in large-scale multi-dimensional databases are discussed. Solving classification, clustering, association rules analysis and regression problems on different kinds of data are covered. Data pre-processing methods for dealing with noisy and missing data in the context of Big Data are reviewed. Evaluation and analysis of data mining models are emphasised. Hands-on case studies in building data mining models are performed using popular modern software packages.

On-campus

Examination (2 hours and 10 minutes): 60%; In-Semester: 40%

6

Minimum total expected workload equals 12 hours per week comprising:

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

(b.) Additional requirements (all students):

FIT

Callista pre-requisite: FIT5047 or FIT5045

Handbook pre-requisite: FIT5047 or FIT5045 or equivalent

For students enrolled in E3001, E3002,E 3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2099 and FIT3171 and FIT3080