Expectation-maximization Clustering
Expectation-Maximization Clustering is a probabilistic clustering method that iteratively refines cluster assignments by alternating between estimating the probability of data points belonging to clusters (Expectation step) and updating the cluster parameters to maximize the likelihood of the observed data (Maximization step).
133
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9.6K
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389
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202
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2017–2026
29
Variants
Em Clustering
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Centre National de la Recherche Scientifique
Paris, France
4 h-index
7 publications
1.1K citations
Teradata (United States)
San Diego, United States
3 h-index
3 publications
114 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
3 publications
114 citations
Université de Technologie de Compiègne
Compiègne, France
3 h-index
4 publications
917 citations
Microsoft (United States)
Redmond, United States
3 h-index
5 publications
721 citations
University of California, Irvine
Irvine, United States
3 h-index
6 publications
380 citations
New York University
New York, United States
2 h-index
2 publications
113 citations
University of Nottingham Ningbo China
Ningbo, China
2 h-index
2 publications
25 citations
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5 publications · 1.6K citations
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Repository · 5 publications · 86 citations
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TPAMI · 3 publications · 1.1K citations
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2 publications · 469 citations
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