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Expectation Maximization Clustering

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Encyclopedia of Machine Learning and Data Mining

Abstract

The expectation maximization (EM) based clustering is a probabilistic method to partition data into clusters represented by model parameters. Extensions to the basic EM algorithm include but not limited to the stochastic EM algorithm (SEM), the simulated annealing EM algorithm (SAEM), and the Monte Carlo EM algorithm (MCEM).

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Recommended Reading

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Author information

Authors and Affiliations

  1. PayPal Inc., San Jose, CA, USA

    Xin Jin

  2. University of Illinios at Urbana-Champaign, 61801, Urbana, IL, USA

    Jiawei Han

Authors
  1. Xin Jin
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  2. Jiawei Han
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Corresponding author

Correspondence to Xin Jin .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Jin, X., Han, J. (2017). Expectation Maximization Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_344

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