An Efficient Cluster Assignment Algorithm for Scaling Support Vector Clustering

Support Vector Clustering (SVC) algorithm reformulates SVM’s Quadratic Programming as a minimum enclosing ball (MEB) problem, where every point in the data space will be projected to the higher dimensional feature space to find the minimum radius that encloses all the data points inside the sphere. Support vectors are data points in the surface of the minimum enclosing ball. The clustering algorithm works by mapping the support vectors of MEB back to the data plane forming groups or derives a contour that encloses a set of clustered points. However, the major limitation of this algorithm is that it fails to scale with larger dataset. Computation bottleneck lies in the efficient cluster computation approach and in solving the QP optimization for MEB. In order to solve this cluster computational complexity, this work presents a simple, efficient cluster assignment algorithm using similarity of feature set for data points in high-dimensional feature space utilizing an efficient MEB approximation algorithm. Experiments are carried out by varying the similarity threshold metrics. Performance of the clustering mechanism run on various datasets demonstrates the proposed algorithm is lighter, simple, and converges faster.