Data mining using rule extraction from Kohonen self-organising maps

Malone, James, McGarry, Kenneth, Wermter, Stefan and Bowerman, Chris (2006) Data mining using rule extraction from Kohonen self-organising maps. Neural Computing & Applications, 15 (1). pp. 9-17. ISSN 0941-0643

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Abstract

The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network’s internal parameters. Such extracted rules can provide a human understandable description of the discovered clusters.

Item Type: Article
Subjects: Computing > Databases
Computing > Information Systems
Computing
Divisions: Digital Innovation Beacon
Faculty of Applied Sciences
Related URLs:
Depositing User: Hannah Dodd
Date Deposited: 22 Dec 2014 16:50
Last Modified: 08 Mar 2017 12:28
URI: http://sure.sunderland.ac.uk/id/eprint/5208

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