Support Vector Clustering
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik;
2(Dec):125-137, 2001.
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.
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