Partitioning Clustering Based on Support Vector Ranking

Qing Peng, Yan Wang, Ge Ou, Lan Huang, Wei Pang

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)
6 Downloads (Pure)


Support Vector Clustering (SVC) has become a significant boundarybased
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.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings
EditorsJinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
PublisherSpringer International Publishing
Number of pages12
ISBN (Electronic)978-3-319-49586-6
ISBN (Print)978-3-319-49585-9
Publication statusPublished - 2016
EventADMA 2016 - Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence


ConferenceADMA 2016
CityGold Coast


  • support vector clustering
  • support vector ranking
  • partitioning clustering


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