Partitioning Clustering Based on Support Vector Ranking

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)

Abstract

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.

Keywords

Support vector clustering Support vector ranking Partitioning clustering 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472159, 61572227), Development Project of Jilin Province of China (20140101180JC,20160204022GX).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.School of Nature and Computing SciencesUniversity of AberdeenAberdeenScotland, UK

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