Parameter modification for clustering criteria

DOI10.1007/BF01896986zbMath0662.62066OpenAlexW1970477726MaRDI QIDQ1113591

Michael P. Windham

Publication date: 1987

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01896986

zbMATH Keywords

classificationpartitioningfuzzy clusteringnumerical optimizationparameter modificationmultivariate mixture analysisnumerical classification

Mathematics Subject Classification ID

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

Related Items

Mutual information, phi-squared and model-based co-clustering for contingency tables, Probabilistic models in cluster analysis, Classification of binary vectors by stochastic complexity., FUZZY SHELL-CLUSTERING AND APPLICATIONS TO CIRCLE DETECTION IN DIGITAL IMAGES, Large-sample results for optimization-based clustering methods, Fuzzy clustering algorithms based on the maximum likelihood principle, Clustering criteria for discrete data and latent class models, A classification EM algorithm for clustering and two stochastic versions

Cites Work

• Local convergence analysis of a grouped variable version of coordinate descent
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
• Clustering Criteria and Multivariate Normal Mixtures
• 389: Separating Mixtures of Normal Distributions
• Asymptotic behaviour of classification maximum likelihood estimates
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