Clustering with r-regular graphs

Authors:
Jong Kyoung Kim;Seungjin Choi
Affiliations:
Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea;Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea
Venue:
Pattern Recognition
Year:
2009

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Abstract

In this paper, we present a novel graph-based clustering method, where we decompose a (neighborhood) graph into (disjoint) r-regular graphs followed by further refinement through optimizing the normalized cluster utility. We solve the r-regular graph decomposition using a linear programming. However, this simple decomposition suffers from inconsistent edges if clusters are not well separated. We optimize the normalized cluster utility in order to eliminate inconsistent edges or to merge similar clusters into a group within the principle of minimal K-cut. The method is especially useful in the presence of noise and outliers. Moreover, the method detects the number of clusters within a pre-specified range. Numerical experiments with synthetic and UCI data sets, confirm the useful behavior of the method.