A possibilistic clustering approach toward generative mixture models

DOI10.1016/j.patcog.2011.10.010zbMath1233.68199OpenAlexW1965039160MaRDI QIDQ763363

Gavriil Tsechpenakis, Sotirios P. Chatzis

Publication date: 9 March 2012

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.10.010

zbMATH Keywords

finite mixture modelspossibilistic clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• UCI-ml
• fminsearch
• PRMLT

Cites Work

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• A method for training finite mixture models under a fuzzy clustering principle
• Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
• Another interpretation of the EM algorithm for mixture distributions
• Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
• 10.1162/jmlr.2003.3.4-5.993
• Fuzzy sets
• On Estimation of a Probability Density Function and Mode

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