Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7620
Title: Expanding self-organizing map for data visualization and cluster analysis
Authors: Jin, Huidong 
Shum, Wing-Ho 
Prof. LEUNG Kwong Sak 
Wong, Man-Leung 
Issue Date: 2004
Source: Information Sciences, 2004, Vol. 163 (1-3), pp. 157 - 173
Journal: Information Sciences 
Abstract: The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2-dimensional grid of neurons with good neighborhood preservation between two spaces. 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 preserve better topology between the two spaces. Besides the neighborhood relationship, our ESOM can detect and preserve an ordering relationship using an expanding mechanism. The computational complexity of the ESOM is comparable with that of the SOM. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM, especially, in terms of the topological error. Furthermore, clustering results generated by the ESOM are more accurate than those obtained by the SOM. © 2003 Elsevier Inc. All rights reserved.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7620
DOI: 10.1016/j.ins.2003.03.020
Appears in Collections:Applied Data Science - Publication

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