Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory

Garg, Vikas K and Narahari, Y and Murty, Narasimha M (2013) Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory. In: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 25 (5). pp. 1070-1082.

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Abstract

We propose a new approach to clustering. Our idea is to map cluster formation to coalition formation in cooperative games, and to use the Shapley value of the patterns to identify clusters and cluster representatives. We show that the underlying game is convex and this leads to an efficient biobjective clustering algorithm that we call BiGC. The algorithm yields high-quality clustering with respect to average point-to-center distance (potential) as well as average intracluster point-to-point distance (scatter). We demonstrate the superiority of BiGC over state-of-the-art clustering algorithms (including the center based and the multiobjective techniques) through a detailed experimentation using standard cluster validity criteria on several benchmark data sets. We also show that BiGC satisfies key clustering properties such as order independence, scale invariance, and richness.

Item Type:	Journal Article
Publication:	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Publisher:	IEEE COMPUTER SOC
Additional Information:	Copy right for this article belongs to the IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
Keywords:	Cooperative game theory; Shapley value; clustering; multiobjective optimization; k-means
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	26 Aug 2015 04:47
Last Modified:	26 Aug 2015 04:47
URI:	http://eprints.iisc.ac.in/id/eprint/52177

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