Neural Computing & Applications

, Volume 15, Issue 1, pp 9–17

Data mining using rule extraction from Kohonen self-organising maps

  • James Malone
  • Kenneth McGarry
  • Stefan Wermter
  • Chris Bowerman
Original Article

DOI: 10.1007/s00521-005-0002-1

Cite this article as:
Malone, J., McGarry, K., Wermter, S. et al. Neural Comput & Applic (2006) 15: 9. doi:10.1007/s00521-005-0002-1

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.

Keywords

Kohonen self-organising Map Rule extraction Data mining Knowledge discovery 

Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  • James Malone
    • 1
  • Kenneth McGarry
    • 1
  • Stefan Wermter
    • 1
  • Chris Bowerman
    • 1
  1. 1.School of Computing and Technology, University of SunderlandSunderlandUK