Self-organizing maps, vector quantization, and mixture modeling

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


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
Citations per Year

265 Citations

Semantic Scholar estimates that this publication has 265 citations based on the available data.

See our FAQ for additional information.

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} }