Fuzzy clustering algorithms based on the maximum likelihood principle

DOI10.1016/0165-0114(91)90147-IzbMath0741.62065OpenAlexW2071962893MaRDI QIDQ1177679

L. Kaufman, E. Trauwaert, Peter J. Rousseeuw

Publication date: 26 June 1992

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-0114(91)90147-i

zbMATH Keywords

global optimumlikelihood principleclustering algorithmslocal optimumfuzzy \(k\)-meansfuzzy isodataproduct of fuzzy determinants

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Probabilistic methods, stochastic differential equations (65C99)

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Cites Work

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• On the meaning of Dunn's partition coefficient for fuzzy clusters
• Parameter modification for clustering criteria
• Pattern classification problems and fuzzy sets
• Numerical taxonomy with fuzzy sets
• Numerical methods for fuzzy clustering
• Bicriterion Cluster Analysis
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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