Model-based overlapping clustering

Model-based overlapping clustering (2005)

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by Arindam Banerjee , Chase Krumpelman , Joydeep Ghosh

Venue:	In KDD
Citations:	18 - 4 self

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Datum	Value	Source
TITLE	Model-based overlapping clustering	INFERENCE
AUTHOR NAME	Arindam Banerjee	SVM HeaderParse 0.2
AUTHOR AFFIL	ABSTRACT; Dept. of Electrical and Computer Engineering; University of Texas at Austin	SVM HeaderParse 0.2
AUTHOR ADDR	Austin, TX 78712, USA	SVM HeaderParse 0.2
AUTHOR NAME	Chase Krumpelman	SVM HeaderParse 0.2
AUTHOR AFFIL	ABSTRACT; Dept. of Electrical and Computer Engineering; University of Texas at Austin	SVM HeaderParse 0.2
AUTHOR ADDR	Austin, TX 78712, USA	SVM HeaderParse 0.2
AUTHOR NAME	Joydeep Ghosh	SVM HeaderParse 0.2
AUTHOR AFFIL	ABSTRACT; Dept. of Electrical and Computer Engineering; University of Texas at Austin	SVM HeaderParse 0.2
AUTHOR ADDR	Austin, TX 78712, USA	SVM HeaderParse 0.2
ABSTRACT	While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters have come from work on analysis of biological datasets. In this paper, we interpret an overlapping clustering model proposed by Segal et al. [23] as a generalization of Gaussian mixture models, and we extend it to an overlapping clustering model based on mixtures of any regular exponential family distribution and the corresponding Bregman divergence. We provide the necessary algorithm modifications for this extension, and present results on synthetic data as well as subsets of 20-Newsgroups and EachMovie datasets.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	In KDD	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	532--537	INFERENCE
CITATIONS	23 found	ParsCit 1.0