Abstract:
The self-organizing map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, neighborhood preservation cannot alway...Show MoreMetadata
Abstract:
The self-organizing map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, neighborhood preservation cannot always lead to perfect topology preservation. In this paper we establish an expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM in terms of both topological and quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those of the SOM.
Date of Conference: 09-12 December 2002
Date Added to IEEE Xplore: 10 March 2003
Print ISBN:0-7695-1754-4