Partitioning Clustering Based on Support Vector Ranking

摘要

Support Vector Clustering (SVC) has become a significant boundary-based clustering algorithm. In this paper we propose a novel SVC algorithm named "Partitioning Clustering Based on Support Vector Ranking (PC-SVR)", which is aimed at improving the traditional SVC, which suffers the drawback of high computational cost during the process of cluster partition. PC-SVR is divided into two parts. For the first part, we sort the support vectors (SVs) based on their geometrical properties in the feature space. Based on this, the second part is to partition the samples by utilizing the clustering algorithm of similarity segmentation based point sorting (CASS-PS) and thus produce the clustering. Theoretically, PC-SVR inherits the advantages of both SVC and CASS-PS while avoids the downsides of these two algorithms at the same time. According to the experimental results, PC-SVR demonstrates good performance in clustering, and it outperforms several existing approaches in terms of Rand index, adjust Rand index, and accuracy index.