Support Vector Clustering with a Novel Cluster Validity Method

Abstract

This paper presents a novel cluster validity method for the support vector clustering (SVC) algorithm to identify an optimal cluster configuration of a given data set. The SVC algorithm is a kernel-based clustering approach that groups a data set into clusters with irregular shapes. Without a priori knowledge of the data sets, a validity measure based on a ratio of cluster compactness to separation with outlier detection has been developed to automatically determine suitable parameters of the kernel functions and soft-margin constants as well. A novel validity measure has been developed to find optimal cluster configurations through an effective parameter searching algorithm. Computer simulations have been conducted on benchmark data sets to demonstrate the effectiveness of the proposed cluster validity method.