Data Mining and Knowledge Discovery - CO832

Location Term Level Credits (ECTS) Current Convenor 2017-18 2018-19
(version 2)
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7 15 (7.5) PROF AA Freitas


CO520 Further Object-Oriented Programming





This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a state-of-the-art data-mining tool, and learn to evaluate the quality of discovered knowledge.


This module appears in:

Contact hours

22 lectures.

Preliminary reading

Main text: IH Witten and E Frank, Data Mining: practical machine learning tools and techniques, 3nd edition, Morgan Kaufmann, 2011

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

Explain the differences between the major data mining tasks, in terms of their assumptions, requirement for a specific kind of data, and the different kinds of knowledge discovered by algorithms performing different kinds of task.
Describe state-of-the-art data mining algorithms for the major data mining tasks.
Identify which data mining task and which algorithm is the most appropriate for a given data mining project, taking into account both the nature of the data to be mined and the goals of the user of the discovered knowledge.
Use a state-of-the-art data mining tool in a principled fashion, being aware of the strengths and weaknesses of the algorithms implemented in the tool.
Evaluate the quality of discovered knowledge, taking into account the requirements of the datamining task being solved.
Describe the main tasks and state-of-the-art algorithms involved in the preprocessing and postprocessing steps of the knowledge discovery process.
Extend data mining concepts and principles to text and web mining.
Utilize the library and exploit web sites to support investigations into these areas.

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