Fast support-based clustering method for large-scale problems

Authors:
Kyu-Hwan Jung;Daewon Lee;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, 790-784 Pohang, Kyungbuk, South Korea;School of Industrial Engineering, University of Ulsan, P.O. Box 18, Ulsan 680-749, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, 790-784 Pohang, Kyungbuk, South Korea
Venue:
Pattern Recognition
Year:
2010

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Abstract

In many support vector-based clustering algorithms, a key computational bottleneck is the cluster labeling time of each data point which restricts the scalability of the method. In this paper, we review a general framework of support vector-based clustering using dynamical system and propose a novel method to speed up labeling time which is log-linear to the size of data. We also give theoretical background of the proposed method. Various large-scale benchmark results are provided to show the effectiveness and efficiency of the proposed method.