An efficient clustering scheme using support vector methods |
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Authors: | J Saketha Nath SK Shevade |
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Affiliation: | a Supercomputer Education and Research Center, Indian Institute of Science, Bangalore-560012, India b Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India |
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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. |
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Keywords: | Clustering Support vector machines R*-tree |
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