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Expanding self-organizing map for data visualization and cluster analysis
Information Sciences
  • Huidong JIN, Chinese University of Hong Kong
  • Wing Ho SHUM, Chinese University of Hong Kong
  • Kwong Sak LEUNG, Chinese University of Hong Kong
  • Man Leung WONG, Lingnan University, Hong Kong
Document Type
Journal article
Publication Date
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.
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Copyright © 2003 Elsevier Inc

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Citation Information
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