A support vector clustering method
Ben-Hur, A.
Horn, D.
Siegelmann, H.T.
Vapnik, V.
Fac. of Ind. Eng. & Manage., Technion-Israel Inst. of Technol., Haifa;
This paper appears in: Pattern Recognition, 2000. Proceedings. 15th International Conference on
Publication Date: 2000
Volume: 2,
On page(s): 724-727 vol.2
Meeting Date: 09/03/2000 - 09/07/2000
Location: Barcelona, Spain
ISBN: 0-7695-0750-6
References Cited: 12
INSPEC Accession Number: 6887564
Digital Object Identifier: 10.1109/ICPR.2000.906177
Current Version Published: 2002-08-06
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
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