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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