American Institute of Mathematical Sciences

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October  2007, 3(4): 619-624. doi: 10.3934/jimo.2007.3.619

A new multimembership clustering method

Yongbin Ou 1, and  Cun-Quan Zhang 1,

1. 

Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310, United States, United States

Received  September 2006 Revised  April 2007 Published  October 2007

Clustering method is one of the most important tools in statistics. In a graph theory model, clustering is the process of finding all dense subgraphs. In this paper, a new clustering method is introduced. One of the most significant differences between the new method and other existing methods is that this new method constructs a much smaller hierarchical tree, which clearly highlights meaningful clusters. Another important feature of the new method is the feature of overlapping clustering or multi-membership. The property of multi-membership is a concept that has recently received increased attention in the literature (Palla, Derényi, Farkas and Vicsek, (Nature 2005); Pereira-Leal, Enright and Ouzounis, (Bioinformatics, 2004); Futschik and Carlisle, (J. Bioinformatics and Computational Biology 2005))
Keywords: overlap clustering, Clustering, dense subgraph., hierarchical clustering, multimembership clustering.
Mathematics Subject Classification: 90C26, 90C4.
Citation: Yongbin Ou, Cun-Quan Zhang. A new multimembership clustering method. Journal of Industrial & Management Optimization, 2007, 3 (4) : 619-624. doi: 10.3934/jimo.2007.3.619
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