Journal
NEURAL COMPUTING & APPLICATIONS
Volume 15, Issue 1, Pages 9-17Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-005-0002-1
Keywords
Kohonen self-organising map; rule extraction; data mining; knowledge discovery
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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.
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