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Algorithms for clustering data
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Vector quantization and signal compression
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A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
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A general probabilistic framework for clustering individuals and objects
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An experimental comparison of model-based clustering methods
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Concept decompositions for large sparse text data using clustering
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Generalized clustering, supervised learning, and data assignment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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Clustering Algorithms
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Bayesian Clustering by Dynamics
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Linear Programming in O([n3/ln n]L) Operations
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| Hi-index | 0.01 |
Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based clustering algorithms. In this view, clusters are represented as probabilistic models in a model space that is conceptually separate from the data space. For partitional clustering, the view is conceptually similar to the Expectation-Maximization (EM) algorithm. For hierarchical clustering, the graph-based view helps to visualize critical/important distinctions between similarity-based approaches and model-based approaches. The framework also suggests several useful variations of existing clustering algorithms. Two new variations---balanced model-based clustering and hybrid model-based clustering---are discussed and empirically evaluated on a variety of data types.