Support vector clustering

• DOI: 10.5555/944790.944807
• Publication Date: 2002-03-01
• Authors: Asa Ben‐Hur; D. Horn; Hava T. Siegelmann; Vladimir Vapnik
• Keywords: Feature vector; Kernel (algebra); Margin (machine learning); Data point

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means Gaussian kernel to high dimensional feature space, where we search for minimal enclosing sphere. This sphere, when back data can separate into several components, each cluster points. simple algorithm identifying these clusters. The width controls scale at which is probed while soft margin constant helps coping with outliers and overlapping structure dataset explored varying two parameters, maintaining number vectors assure smooth boundaries. demonstrate performance our on datasets.