Data Mining and Knowledge Discovery - CO832

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2020 to 2021
Canterbury
(version 2)
Spring 7 15 (7.5) PROF A Freitas checkmark-circle

Overview

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 data mining tool, and learn to evaluate the quality of discovered knowledge.

Details

This module appears in the following module collections.

Contact hours

Total contact hours: 22 hours
Private study hours: 128 hours
Total study hours: 150 hours

Method of assessment

13.1 Main assessment methods
One exercise with a data mining tool 10%
One Short Essay (about 1,000 words) 10%
Examination 80%

13.2 Reassessment methods
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Indicative reading

Witten, IH, Frank, E, Hall, MA, Pal, CJ (2016). Data Mining: practical machine learning tools and techniques, 4rd edition. Morgan Kaufmann.
Tan, P-N, Steinbach, M, Karpatne, A, Kumar, V (2018) Introduction to Data Mining, Pearson, 2nd edition.

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 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.
8.2 Describe data mining algorithms for the major data mining tasks.
8.3 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.
8.4 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.
8.5 Evaluate the quality of discovered knowledge, taking into account the requirements of the data mining task being solved and the goals of the user.
8.6 Describe the main tasks and algorithms involved in the preprocessing and postprocessing steps of the knowledge discovery process.
8.7 Utilize the library and exploit web sites to support investigations into these areas.

9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Understand the major kinds of data mining tasks and the main kinds of algorithms that are often used to solve these tasks.
9.2 Understand the strengths and weaknesses of some data mining algorithms, identifying the kind of algorithm that is most appropriate for each data mining problem.
9.3 Understand the process of knowledge discovery, involving not only data mining but also preprocessing and post-processing steps

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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