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Generative Models for Evolutionary Clustering

By Tianbing Xu, Zhongfei Zhang, Philip S. Yu and Bo Long

Abstract

This paper studies evolutionary clustering, a recently emerged hot topic with many important applications, noticeably in dynamic social network analysis. In this paper, based on the recent literature on Nonparametric Bayesian models, we have developed two generative models DPChain and HDP-HTM. DPChain is derived from the Dirichlet Process Mixture (DPM) model with an exponential decaying component along with the time. HDP-HTM combines the Hierarchical Dirichlet Process (HDP) with a Hierarchical Transition Matrix (HTM) based on the proposed Infinite Hierarchical Markov State model (iHMS). 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 explicitly addressing the correspondence issues over the evolution. Extensive evaluations have demonstrated the effectiveness and the promise of these two solutions against the state-of-the-art literature

Topics: Categories and Subject Descriptors, H.2.8 [Database Applications, Data mining, G.3 [Probability and Statistics, Nonparametric statistics, H.3.3 [Information Storage and Retrieval, Information Search and Retrieval—Clustering General Terms, Algorithms Additional Key Words and Phrases, Evolutionary Clustering, DPChain, HDP-HTM, iHMS, Hierarchical Transition Matrix
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.306.5717
Provided by: CiteSeerX
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