Support vector clustering

  • Authors:
  • Asa Ben-Hur;David Horn;Hava T. Siegelmann;Vladimir Vapnik

  • Affiliations:
  • BIOwulf Technologies, 2030 Addison st. suite 102, Berkeley, CA;School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel;Lab for Information and Decision Systems, MIT Cambridge, MA;AT&T Labs Research, 100 Schultz Dr., Red Bank, NJ

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2002

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Abstract

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.