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.
Original language | English |
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Pages (from-to) | 157-173 |
Number of pages | 17 |
Journal | Information Sciences |
Volume | 163 |
Issue number | 1-3 |
DOIs | |
Publication status | Published - 14 Jun 2004 |
Funding
The work described in this paper was supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region with projects CuHK u212/01E and Lu 3009/02E. The authors appreciate the anonymous reviewers for their valuable comments to strengthen the paper.