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


Download (288kB)

Search Google Scholar


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
Divisions: Faculty of Technology
Faculty of Technology > School of Computer Science
Faculty of Technology > FOT Executive
Related URLs:
Depositing User: Hannah Dodd
Date Deposited: 22 Dec 2014 16:50
Last Modified: 18 Dec 2019 15:37
ORCID for Kenneth McGarry: ORCID iD

Actions (login required)

View Item View Item


Downloads per month over past year