Clustering using adaptive self-organizing maps (ASOM) and applications

Authors:
Yong Wang;Chengyong Yang;Kalai Mathee;Giri Narasimhan
Affiliations:
Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, FL;Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, FL;Department of Biological Sciences, Florida International University, Miami, FL;Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, FL
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

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Abstract

This paper presents an innovative, adaptive variant of Kohonen's self-organizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on the best architecture for the self-organizing map. Like the traditional SOMs, this clustering technique also provides useful information about the relationship between the resulting clusters. Applications of the resulting software to clustering biological data are discussed in detail.