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 are 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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Jin, X., Han, J. (2023). Expectation Maximization Clustering. In: Phung, D., Webb, G.I., Sammut, C. (eds) Encyclopedia of Machine Learning and Data Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7502-7_344-2
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_344-2
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Chapter history
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Latest
Expectation Maximization Clustering- Published:
- 12 April 2023
DOI: https://doi.org/10.1007/978-1-4899-7502-7_344-2
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Original
Expectation Maximization Clustering- Published:
- 14 June 2016
DOI: https://doi.org/10.1007/978-1-4899-7502-7_344-1