A Graph Clustering Algorithm Based on Minimum and Normalized Cut

Authors:
Jiabing Wang;Hong Peng;Jingsong Hu;Chuangxin Yang
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China and Guangdong University of Commerce, Guangzhou 510320, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

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Abstract

Clustering is the unsupervised classification of patterns into groups. In this paper, a clustering algorithm for weighted similarity graph is proposed based on minimum and normalized cut. The minimum cut is used as the stopping condition of the recursive algorithm, and the normalized cut is used to partition a graph into two subgraphs. The algorithm has the advantage of many existing algorithms: nonparametric clustering method, low polynomial complexity, and the provable properties. The algorithm is applied to image segmentation; the provable properties together with experimental results demonstrate that the algorithm performs well.