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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

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

Item Type: Article


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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Depositing User: Hannah Dodd


Item ID: 5208
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ISSN: 0941-0643
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ORCID for Kenneth McGarry: ORCID iD

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Date Deposited: 22 Dec 2014 16:50
Last Modified: 18 Dec 2019 15:37


Author: Kenneth McGarry ORCID iD
Author: James Malone
Author: Stefan Wermter
Author: Chris Bowerman

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science
Faculty of Technology > FOT Executive


Computing > Databases
Computing > Information Systems

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