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Entropy-type classification maximum likelihood algorithms for mixture models
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DOI：10.1007/s00500-010-0560-8.png)
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Abstract
En 中文
Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods.
Keywords:
Classification maximum likelihood (CML)
Fuzzy clustering
Fuzzy CML
Entropy
Entropy-type CML
Parameter-free
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