Module delivery information

This module is not currently running in 2024 to 2025.


Data mining is a process of extracting, from a large amount of data, interesting patterns that are non-trivial, hidden, new and potentially useful. It is a rapidly growing field and is becoming important because with the increasing quantity and variety of online data collections by many organizations and commercial enterprises, there is a high potential value of patterns discovered in those collections.

This module looks at different data mining techniques and gives you the chance to use a state-of-the-art data-mining tool and evaluate the quality of the discovered knowledge. The topics include: introduction to data mining and knowledge discovery process, data description, , data pre-processing, attribute selection, market basket analysis and association rules, classification, clustering, outlier detection, post-processing, social impact and trend of data mining.


Contact hours

Total contact hours: 28 hours
Private study hours: 122 hours
Total study hours: 150 hours

Method of assessment

Main assessment methods
Coursework - 50%, Examination - 50%

Indicative reading

Berry, M., and Linoff, G. (2012). Data Mining Techniques: For Marketing, Sales and Customer Relationship Management.
Bramer, M. (2007). Principles of Data Mining.
Han, J., and Kamber, M. (2012). Data Mining: concepts and techniques.
Tan, P., Steinbachm, M., and Kumar, V. (2013). Introduction to Data Mining.
Witten, I. H., and Frank, E. (2011). Data Mining: Practical Machine Learning Tools and Techniques.

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1 Understand the motivation for data mining in the context of business and information technology
2 Know how data mining is used, particularly for marketing, sales and customer relationship management
3 Understand the concepts and main techniques in data mining
4 Be able to describe the differences between the major data mining tasks
5 Have an understanding of the knowledge discovery process
6 Understand the purpose of the main tasks involved in data preparation for mining
7 Gain hands-on experience in using a state-of-the-art data mining tool

The intended generic learning outcomes. On successfully completing the module students will be able to:
1 Analyse a problem, design and find a solution
2 Make succinct presentation to a range of audience
3 Make effective use of IT facilities
4 Manage their own learning and time ]


  1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  2. The named convenor is the convenor for the current academic session.
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