Data mining using rule extraction from Kohonen self-organising maps

James Malone , Ken McGarry , Stefan Wermter , Chris Bowerman
Neural Computing & Applications, Volume 15, Number 1, pages 9--17, doi: 10.1007/s00521-005-0002-1 - Mar 2006
Associated documents :  
The Kohonen self-organizing 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.

 

@Article{MMWB06,
 	 author =  {Malone, James and McGarry, Ken and Wermter, Stefan and Bowerman, Chris},
 	 title = {Data mining using rule extraction from Kohonen self-organising maps},
 	 booktitle = {None},
 	 journal = {Neural Computing & Applications},
 	 editors = {None},
 	 number = {1},
 	 volume = {15},
 	 pages = {9--17},
 	 year = {2006},
 	 month = {Mar},
 	 publisher = {Springer},
 	 doi = {10.1007/s00521-005-0002-1},
 }