S. Zhong and J. Ghosh. A unified framework for modelbased clustering and its applications to clustering time sequences. Technical report, ECE Dept., The University of Texas at Austin, 2002.  Home/Search Document Not in Database Summary Related Articles Check |
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Distributed Clustering with Limited Knowledge Sharing - Ghosh, Merugu Self-citation (Ghosh) (Correct)
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S. Zhong and J. Ghosh. A unified framework for modelbased clustering and its applications to clustering time sequences. Technical report, ECE Dept., The University of Texas at Austin, 2002.
Scalable, Balanced Model-based Clustering - Zhong, Ghosh Self-citation (Zhong Ghosh) (Correct)
....a minimum number of data samples at each iteration. The cluster assignment subproblem was formulated as a minimum cost flow problem, which has a high (O(n ) complexity. In this paper, we take a balance constrained approach built upon the framework of probabilistic, model based clustering [40]. Model based clustering is very general, and can be used to cluster a wide variety of data types, from vector data to variable length sequences of symbols or numbers [29] First, a unifying bipartite graph view is presented for model based clustering. Then a two step iterative maximum likelihood ....
....generic model based clustering algorithms can be readily applied. The closeness measure is used for assigning samples to clusters in partitional clustering and the distance measure for finding the closest pair of clusters to merge in hierarchical agglomerative clustering. Readers are referred to [40] for a detailed description. The maximum likelihood method is often used for training a model # given a set of data samples O, to maximize P (O #) or equivalently, log P (O #) A model based clustering algorithm usually iteratively partitions the data samples and trains the models based on the ....
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S. Zhong and J. Ghosh. A unified framework for modelbased clustering and its applications to clustering time sequences. Technical report, ECE Dept., The University of Texas at Austin, May 2002.
Clustering Time Series from Mixture Polynomial.. - Bagnall, Janacek.. (2003) (Correct)
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Shi Zhong and Joydeep Ghosh. A unified framework for model-based clustering and its application to clustering time sequences. Technical report, Department of Electrical and Computer Engineering, University of Texas, 2002.
Clustering Time Series from Mixture Polynomial Models.. - Bagnall, Janacek, Zhang (2003) (Correct)
No context found.
Shi Zhong and Joydeep Ghosh. A unified framework for model-based clustering and its application to clustering time sequences. Technical report, Department of Electrical and Computer Engineering, University of Texas, 2002.
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