NDLI: Evolutionary Clustering by Hierarchical Dirichlet Process with Hidden Markov State

Content Provider	IEEE Xplore Digital Library
Author	Tianbing Xu Zhongfei Zhang Yu, P.S. Long, B.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL (Yu, P.S.) \|\| Dept. of Comput. Sci., State Univ. of New York at Binghamton, Binghamton, NY (Tianbing Xu; Zhongfei Zhang; Long, B.)
Abstract	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 $(iH^{2}MS)$ 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.
Starting Page	658
Ending Page	667
File Size	290014
Page Count	10
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Data mining Computer science USA Councils Social network services Application software Information services Web sites Internet Time sharing computer systems HDP-HTM Evolutionary Clustering Hierarchical Dirichlet Process Infinite Hidden Markov Model
Content Type	Text
Resource Type	Article

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