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Scalable Model-based Clustering by Working on Data Summaries
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A unified framework for model-based clustering
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Hierarchical Latent Class Models for Cluster Analysis
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Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
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MMPClust: a skew prevention algorithm for model-based document clustering
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Agglomerative hierarchical clustering methods based on Gaussian probability models have recently shown promise in a variety of applications. In this approach, a maximum-likelihood pair of clusters is chosen for merging at each stage. Unlike classical methods, model-based methods reduce to a recurrence relation only in the simplest case, which corresponds to the classical sum of squares method. We show how the structure of the Gaussian model can be exploited to yield efficient algorithms for agglomerative hierarchical clustering.