Self-organizing maps, vector quantization, and mixture modeling.
Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive expectation-maximization (EM) algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional data. Several extensions and improvements are discussed. As an illustration we apply a self-organizing map based on a multinomial distribution to market basket analysis.
Heskes
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T
RWCP Theoretical Foundation SNN, University of Nijmegen, Nijmegen 6252 EZ, The Netherlands. tom@mbfys.kun.nl