Abstract
This subject provides in-depth coverage of the principles of data mining and the traditional machine learning techniques used for deriving business intelligence. It focuses on the need for developing timely and accurate descriptive and predictive models from large datasets to present accurate views of complex data trends and patterns. Students … For more content click the Read More button below.
Syllabus
Introduction to Data MiningData interpretation for machine learningCredibility of modelsProbabilistic modellingDecision TreesClassification RulesAssociation RulesLinear ModelsInstance-Based Learning and ClusteringFurther topics in data mining
Learning outcomes
Upon successful completion of this subject, students should:
1.
be able to identify and analyse business requirements for the identification of patterns and trends in data sets;
2.
be able to appraise the different approaches and categories of data mining problems;
3.
be able to compare and evaluate output patterns;
4.
be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements;
5.
be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set;
6.
be able to identify and evaluate the security, privacy and ethical implications in data mining; and
7.
be able to explain the importance of current and future trends likely to affect data mining and visualisation.
Assumed knowledge
Familiarity with concepts of foundational mathematics (e.g., algebra and probability) and basic understanding of database systems.
Enrolment restrictions
Only available to postgraduate students.