@article {IOPORT.05027621,
    author       = {Malone, James and McGarry, Kenneth and Wermter, Stefan and Bowerman, Chris},
    title        = {Data mining using rule extraction from kohonen self-organising maps.},
    year         = {2006},
    journal      = {Neural Computing and Applications},
    volume       = {15},
    number       = {1},
    issn         = {0941-0643},
    pages        = {9-17},
    publisher    = {Springer-Verlag, London},
    doi          = {10.1007/s00521-005-0002-1},
    abstract     = {Summary: 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.},
    identifier   = {05027621},
}