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Semester 2, 2011

Convenor: Diego Molla-Aliod

Prerequisites: -

Students should read the unit outline carefully at the start of semester. It contains important information about the unit. If anything in it is unclear, please consult one of the teaching staff in the unit.

About This Unit

Large data collections are widely available in many companies and organisations.

Data mining is still a young multidisciplinary field which involves Database and data warehouse technologies, Machine learning and artificial intelligence, Statistics and numerical mathematics, Parallel and high-performance computing and Visualisation.

Data mining is being applied in many areas such as in Bioinformatics and health, Governments (statistics, census and taxation), Credit card and insurance companies, Terror, crime and fraud detection, and in Networking and telecommunications.

ITEC872 covers the relationship that can be established between data mining and smart business practices. It also explains how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence techniques. The course covers both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis in data mining and how we can apply these methods to real business cases and data.

Teaching Staff

Role	Name	Email
Convenor, Lecturer	Dr Diego Molla-Aliod	diego.molla-aliod@mq.edu.au

All emails related to ITEC872 should be sent to Diego Molla-Aliod and must include your full name and your student id number.

Unit outline

You may access the unit outline here.

Course material

By logging on at http://ilearn.mq.edu.au/, you will have access to the course material.