Self-organizing maps, vector quantization, and mixture modeling

  • Tom Heskes
  • Published 2001 in IEEE Trans. Neural Networks

Abstract

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.

DOI: 10.1109/72.963766
02040'03'05'07'09'11'13'15'17
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Cite this paper

@article{Heskes2001SelforganizingMV, title={Self-organizing maps, vector quantization, and mixture modeling}, author={Tom Heskes}, journal={IEEE transactions on neural networks}, year={2001}, volume={12 6}, pages={1299-305} }