View article

Support Vector Machines (SVMs) have been used in many areas such as regression, classification and novelity detection due to its accuracy and generalizability. Recently SVMs have been proposed for clustering analysis as well. Support Vector Clustering (SVC) works by finding the minimum enclosing sphere of data points using SVM training. SVC is a boundary based clustering method, where the support information is used to construct cluster boundaries. In support vector-based clustering algorithms, the main computational bottle-neck is the high cluster labeling time for each data point. In addition, in many cases labeled data is not available for use with SVC. This tends to restrict the scalability of the method and results in decreased efficiency. This also decreases the applicability of the SVC method to real-life datasets most of which do not have any class labels.. In this paper we present a technique that could …