COMP7707 Advanced Data Mining and Knowledge Discovery

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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 processes and frameworks have emerged as potential solutions. Specifically the topic will cover: Introduction - The role of common sense, trends in information management, fundamental ideas, developing data mining algorithms, applications of knowledge discovery, future directions in DMKD. Data mining techniques - association rule mining, clustering algorithms, classification and prediction, sequential pattern mining, graph mining, text mining, higher order data mining, visualisation techniques, spatial data mining, temporal and longitudinal data mining, interestingness, web mining, ethics in data mining, knowledge discovery frameworks, research methods used in data mining and knowledge discovery.

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:

Understand the potential and limitations of given data mining approaches

Use common algorithms used in available systems and in the literature

Understand the ethical and legal issues involved

Understand the theoretical fundamentals underpinning the field

Construct rudimentary knowledge discovery systems

Argue the merits of various research issues in the field

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.

Handbook

Advanced Data Mining and Knowledge Discovery