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Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory
May 2013 (vol. 25 no. 5)
pp. 1070-1082
Vikas K. Garg, Toyota Technological Institute at Chicago, Chicago
Y. Narahari, Indian Institute of Science (IISc), Bangalore
M. Narasimha Murty, Indian Institute of Science (IISc), Bangalore
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.
Index Terms:
Games,Clustering algorithms,Resource management,Data models,Analytical models,Heuristic algorithms,Game theory,$(k)$-means,Cooperative game theory,Shapley value,clustering,multiobjective optimization
Citation:
Vikas K. Garg, Y. Narahari, M. Narasimha Murty, "Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 5, pp. 1070-1082, May 2013, doi:10.1109/TKDE.2012.73
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