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Unsupervised clustering using nonparametric finite mixture models
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DOI：10.1002/wics.1632.png)
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This article presents basic ideas of finite mixture models in which the number of components is known and the distributions comprising the components are not assumed to come from any parametrically specified family.This article is categorized under:Algorithms and Computational Methods > AlgorithmsStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and ClassificationStatistical and Graphical Methods of Data Analysis > Nonparametric MethodsStatistical Models > Classification Models
Keyword:
EM algorithm
kernel density estimation
semiparametric mixture
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201
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