A Self-Organizing Map with Expanding Force for Data Clustering and Visualization Wing-Ho Shum, Huidong Jin, Kwong-Sak Leung, and Man Leung Wong   View Paper (PDF)   Return to Main-Track Regular Papers 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. DiSC'03 © 2003 Association for Computing Machinery