Expanding self-organizing map for data visualization and cluster analysis

Huidong JIN, Wing Ho SHUM, Kwong Sak LEUNG, Man Leung WONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

53 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)157-173
Number of pages17
JournalInformation Sciences
Issue number1-3
Publication statusPublished - 14 Jun 2004


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