@inproceedings{54449a2282774ab6824589f57c951896,

title = "A support vector method for clustering",

abstract = "We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere enclosing them. The boundary of the sphere forms in data space a set of closed contours containing the data. Data points enclosed by each contour are defined as a cluster. As the width parameter of the Gaussian kernel is decreased, these contours fit the data more tightly and splitting of contours occurs. The algorithm works by separating clusters according to valleys in the underlying probability distribution, and thus clusters can take on arbitrary geometrical shapes. As in other SV algorithms, outliers can be dealt with by introducing a soft margin constant leading to smoother cluster boundaries. The structure of the data is explored by varying the two parameters. We investigate the dependence of our method on these parameters and apply it to several data sets.",

author = "Asa Ben-Hur and David Horn and Siegelmann, {Hava T.} and Vladimir Vapnik",

year = "2001",

language = "אנגלית",

isbn = "0262122413",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000",

note = "null ; Conference date: 27-11-2000 Through 02-12-2000",

}