Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7640
Title: A self-organizing map with expanding force for data clustering and visualization
Authors: Shum, Wing-Ho 
Jin, Hui-Dong 
Prof. LEUNG Kwong Sak 
Wong, Man-Leung 
Issue Date: 2002
Source: Proceedings - IEEE International Conference on Data Mining, ICDM, 2002, pp. 434 - 441
Journal: Proceedings - IEEE International Conference on Data Mining, ICDM 
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. © 2002 IEEE.
Type: Conference Proceedings
URI: http://hdl.handle.net/20.500.11861/7640
ISBN: 0769517544
978-076951754-4
ISSN: 15504786
Appears in Collections:Applied Data Science - Publication

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