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2008
IEEE

Dirichlet Process Based Evolutionary Clustering

10 years 1 months ago
Dirichlet Process Based Evolutionary Clustering
Evolutionary Clustering has emerged as an important research topic in recent literature of data mining, and solutions to this problem have found a wide spectrum of applications, particularly in social network analysis. In this paper, based on the recent literature on Dirichlet processes, we have developed two different and specific models as solutions to this problem: DPChain and HDP-EVO. Both models substantially advance the literature on evolutionary clustering in the sense that not only they both perform better than the existing literature, but more importantly they are capable of automatically learning the cluster numbers and structures during the evolution. Extensive evaluations have demonstrated the effectiveness and promise of these models 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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