Overview

Data Mining (DM) and Knowledge Discovery (KD) are concerned with the extraction of useful knowledge from large quantities of more or less structured information. With the continued growth in large data sets and the inability of manual analytical techniques to cope with such volumes, data mining algorithms and knowledge discovery … For more content click the Read More button below.

Topic availabilities

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Tuition pattern

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Aims

On completion of this topic, students will have gained knowledge in:

  • The theoretical foundations of induction in data-rich environments
  • The algorithms commonly used in data mining
  • The architectural frameworks commonly used in knowledge discovery
  • Some of the legal and ethics considerations of data mining and knowledge discovery
  • The research methods used in data mining and knowledge discovery
  • Current research issues in data mining and knowledge discovery

Learning outcomes

On completion of this topic you will be expected to be able to:
1.
Understand the potential and limitations of given data mining approaches
2.
Use common algorithms used in available systems and in the literature
3.
Understand the ethical and legal issues involved
4.
Understand the theoretical fundamentals underpinning the field
5.
Construct rudimentary knowledge discovery systems
6.
Argue the merits of various research issues in the field

Assessments

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Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.

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Requisites information

Pre-requisites:
Anti-requisites:

Assumed knowledge

Computing skills such as can be obtained in introductory computing and database topics. Programming knowledge and skills such as can be obtained in second year level programming topics. A knowledge of discrete mathematics and/or intelligent systems would also be useful.