Generative Models for Evolutionary Clustering

Authors:
Tianbing Xu;Zhongfei Zhang;Philip S. Yu;Bo Long
Affiliations:
State University of New York at Binghamton;State University of New York at Binghamton and Zhejiang University;University of Illinois at Chicago;Yahoo! Inc.
Venue:
ACM Transactions on Knowledge Discovery from Data (TKDD)
Year:
2012

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Abstract

This article studies evolutionary clustering, a recently emerged hot topic with many important applications, noticeably in dynamic social network analysis. In this article, 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 do they both perform better than those in the existing literature, but more importantly, they are capable of automatically learning the cluster numbers and explicitly addressing the corresponding issues. Extensive evaluations have demonstrated the effectiveness and the promise of these two solutions compared to the state-of-the-art literature.