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# Model-Based Clustering by Probabilistic Self-Organizing Maps

@article{Cheng2009ModelBasedCB, title={Model-Based Clustering by Probabilistic Self-Organizing Maps}, author={Shih-Sian Cheng and Hsin-Chia Fu and Hsin-Min Wang}, journal={IEEE Transactions on Neural Networks}, year={2009}, volume={20}, pages={805-826} }

- Published 2009 in IEEE Transactions on Neural Networks
DOI:10.1109/TNN.2009.2013708

In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a coupling-likelihood mixture model for the PbSOM that extends the reference vectors in Kohonen's self-organizing map (SOM) to multivariate Gaussian distributions. We also derive three expectation-maximization (EM)-type algorithms, called… CONTINUE READING

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