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Introduction to the Use of Mixture Models in Clustering
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The article was published on 1986-12-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Cluster analysis & Mixture model.read more
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
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Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
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Cluster Analysis
TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.
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An Introduction to Multivariate Statistical Analysis
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.