Data mining using rule extraction from Kohonen self-organising maps

Authors:
James Malone;Kenneth McGarry;Stefan Wermter;Chris Bowerman
Affiliations:
School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, SR6 ODD, Sunderland, UK;School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, SR6 ODD, Sunderland, UK;School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, SR6 ODD, Sunderland, UK;School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, SR6 ODD, Sunderland, UK
Venue:
Neural Computing and Applications
Year:
2006

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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.