A support vector clustering method
Asa Ben‐Hur, D. Horn, Hava T. Siegelmann, Vladimir N. Vapnik · 2002
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.