Expanding self-organizing map for data visualization and cluster analysis
Document Type
Journal article
Source Publication
Information Sciences
Publication Date
6-14-2004
Volume
163
Issue
1-3
First Page
157
Last Page
173
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 2dimensional 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.
DOI
10.1016/j.ins.2003.03.020
Print ISSN
00200255
E-ISSN
18726291
Publisher Statement
Copyright © 2003 Elsevier Inc
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Jin, H., Shum, W.-H., Leung, K.-S., & Wong, M.-L. (2004). Expanding self-organizing map for data visualization and cluster analysis. Information Sciences, 163(1-3), 157-173. doi: 10.1016/j.ins.2003.03.020