Local convergence of the fuzzy c-means algorithms

DOI10.1016/0031-3203(86)90047-6zbMath0623.62058OpenAlexW2039024677MaRDI QIDQ1091715

Richard J. Hathaway, James C. Bezdek

Publication date: 1986

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0031-3203(86)90047-6

zbMATH Keywords

rate of convergencepattern recognitioncluster analysisFCM algorithmfuzzy c-meansiteration sequences started near a solutionlocal convergence property

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonlinear programming (90C30) Probabilistic methods, stochastic differential equations (65C99)

Related Items (9)

Convergence properties of the generalized fuzzy c-means clustering algorithms ⋮ Optimality tests for the fuzzy \(c\)-means algorithm ⋮ Optimality tests for fixed points of the fuzzy c-means algorithm ⋮ A new stochastic gradient descent possibilistic clustering algorithm ⋮ Projected Gustafson-Kessel Clustering Algorithm and Its Convergence ⋮ Coresets for Fuzzy K-Means with Applications ⋮ Analytically tractable case of fuzzy c-means clustering ⋮ Clustering categorical data sets using tabu search techniques ⋮ A survey of fuzzy clustering

Cites Work

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• Local convergence analysis of a grouped variable version of coordinate descent
• K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
• Global convergence and empirical consistency of the generalized Lloyd algorithm
• A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
• Detection and Characterization of Cluster Substructure II. Fuzzy c-Varieties and Convex Combinations Thereof
• Quantization and the method of<tex>k</tex>-means
• Convergence Conditions for Nonlinear Programming Algorithms
• Cluster Validity with Fuzzy Sets

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