Extending fuzzy and probabilistic clustering to very large data sets

DOI10.1016/j.csda.2006.02.008zbMath1157.62435OpenAlexW2064128636MaRDI QIDQ1010358

Richard J. Hathaway, James C. Bezdek

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.02.008

zbMATH Keywords

clusteringgoodness-of-fitdata miningfuzzy \(c\)-meansextensibilityprogressive samplingvery large data sets

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (6)

Bounded fuzzy possibilistic method ⋮ Scalable visual assessment of cluster tendency for large data sets ⋮ Fuzzy \(c\)-means and cluster ensemble with random projection for big data clustering ⋮ The fuzzy approach to statistical analysis ⋮ A review on suppressed fuzzy c-means clustering models ⋮ Approximate clustering in very large relational data

Cites Work

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• Cure: An efficient clustering algorithm for large databases
• Parametric estimation for normal mixtures
• Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
• On the asymptotic properties of fuzzy c-means cluster prototypes as estimators of mixture subpopulation centers
• Statistical analysis of finite mixture distributions
• Optimal Fuzzy Partitions: A Heuristic for Estimating the Parameters in a Mixture of Normal Distributions
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finite mixture models
• 10.1162/153244302760200678

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