An efficient clustering scheme using support vector methods

Authors:
J. Saketha Nath;S. K. Shevade
Affiliations:
Supercomputer Education and Research Center, Indian Institute of Science, Bangalore-560012, India;Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India
Venue:
Pattern Recognition
Year:
2006

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Abstract

Support vector clustering involves three steps-solving an optimization problem, identification of clusters and tuning of hyper-parameters. In this paper, we introduce a pre-processing step that eliminates data points from the training data that are not crucial for clustering. Pre-processing is efficiently implemented using the R*-tree data structure. Experiments on real-world and synthetic datasets show that pre-processing drastically decreases the run-time of the clustering algorithm. Also, in many cases reduction in the number of support vectors is achieved. Further, we suggest an improvement for the step of identification of clusters.