Ensemble based support vector clustering

Abstract

Hyper-plane based Support Vector Machine (SVM) for classification and hyper-spherical based Support Vector Data Description (SVDD) for clustering have been shown to be very effective in many real-world machine learning problems in various domains. Support vector clustering (SVC) based on SVDD has been successfully used to achieve robust clustering. However, in most cases, it is not possible to choose the best of input parameter values for an input dataset which restricts the application of SVC. Only a few heuristic approaches have been proposed to set the different parameters of these methods in an unsupervised manner. In this paper, we propose an ensemble based clustering approach to tackle the automatic parameter estimation problem for SVC in a fully unsupervised manner which is capable of producing robust clustering results. The experiments on multiple real-world datasets demonstrate the effectiveness of our approach.