A support vector clustering method

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel kernel method for data clustering using a description of the data by support vectors. The kernel reflects a projection of the data points from data space to a high dimensional feature space. Cluster boundaries are defined as spheres in feature space, which represent complex geometric shapes in data space. We utilize this geometric representation of the data to construct a simple clustering algorithm.

Original languageEnglish
Pages (from-to)724-727
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume15
Issue number2
StatePublished - 2000

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