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2002 IEEE International Conference on Data Mining, 2002. Proceedings. (2002)
Maebashi City, Japan
Dec. 9, 2002 to Dec. 12, 2002
ISBN: 0-7695-1754-4
pp: 434
Wing-Ho Shum , Chinese University of Hong Kong
Hui-Dong Jin , Chinese University of Hong Kong
Kwong-Sak Leung , Chinese University of Hong Kong
Man-Leung Wong , Lingnan University
ABSTRACT
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the 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 the topological and the quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those by the SOM.
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CITATION

H. Jin, M. Wong, W. Shum and K. Leung, "A Self-Organizing Map with Expanding Force for Data Clustering and Visualization," 2002 IEEE International Conference on Data Mining, 2002. Proceedings.(ICDM), Maebashi City, Japan, 2002, pp. 434.
doi:10.1109/ICDM.2002.1183939
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