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2008
IEEE

Evolutionary Clustering by Hierarchical Dirichlet Process with Hidden Markov State

10 years 1 months ago
Evolutionary Clustering by Hierarchical Dirichlet Process with Hidden Markov State
This paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. In this paper, based on the recent literature on Hierarchical Dirichlet Process (HDP) and Hidden Markov Model (HMM), we have developed a statistical model HDP-HTM that combines HDP with a Hierarchical Transition Matrix (HTM) based on the proposed Infinite Hierarchical Hidden Markov State model (iH2 MS) as an effective solution to this problem. The HDP-HTM model substantially advances the literature on evolutionary clustering in the sense that not only it performs better than the existing literature, but more importantly it is capable of automatically learning the cluster numbers and structures and at the same time explicitly addresses the correspondence issue during the evolution. Extensive evaluations have demonstrated the effectiveness and promise of this solution against the state-of-the-art literature.
Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu,
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu, Bo Long
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