A Support Vector Method for Hierarchical Clustering
Asa Ben‐Hur · 2007
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. As the kernel parameter is varied these contours fit the data more tightly and splitting of contours occurs. The contours are interpreted as cluster boundaries and the points within each disconnected contour are defined as a cluster. Cluster boundaries can take on arbitrary geometrical shapes and clusters are separated by valleys in the underlying probability distribution. As in other SV algorithms, outliers can be dealt with by introducing a soft margin constant leading to smoother cluster boundaries. The hierarchical 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 s...