TY - GEN
T1 - Partitioning Clustering Based on Support Vector Ranking
AU - Peng, Qing
AU - Wang, Yan
AU - Ou, Ge
AU - Tian, Yuan
AU - Huang, Lan
AU - Pang, Wei
PY - 2016
Y1 - 2016
N2 - Support Vector Clustering (SVC) has become a significant boundarybasedclustering algorithm. In this paper we propose a novel SVC algorithmnamed “Partitioning Clustering Based on Support Vector Ranking (PC-SVR)”,which is aimed at improving the traditional SVC, which suffers the drawback ofhigh 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) basedon their geometrical properties in the feature space. Based on this, the secondpart is to partition the samples by utilizing the clustering algorithm of similaritysegmentation based point sorting (CASS-PS) and thus produce the clustering.Theoretically, PC-SVR inherits the advantages of both SVC and CASS-PSwhile avoids the downsides of these two algorithms at the same time. Accordingto the experimental results, PC-SVR demonstrates good performance inclustering, and it outperforms several existing approaches in terms of Rand index,adjust Rand index, and accuracy index.
AB - Support Vector Clustering (SVC) has become a significant boundarybasedclustering algorithm. In this paper we propose a novel SVC algorithmnamed “Partitioning Clustering Based on Support Vector Ranking (PC-SVR)”,which is aimed at improving the traditional SVC, which suffers the drawback ofhigh 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) basedon their geometrical properties in the feature space. Based on this, the secondpart is to partition the samples by utilizing the clustering algorithm of similaritysegmentation based point sorting (CASS-PS) and thus produce the clustering.Theoretically, PC-SVR inherits the advantages of both SVC and CASS-PSwhile avoids the downsides of these two algorithms at the same time. Accordingto the experimental results, PC-SVR demonstrates good performance inclustering, and it outperforms several existing approaches in terms of Rand index,adjust Rand index, and accuracy index.
KW - support vector clustering
KW - support vector ranking
KW - partitioning clustering
UR - https://www.scopus.com/pages/publications/85000470550
U2 - 10.1007/978-3-319-49586-6_52
DO - 10.1007/978-3-319-49586-6_52
M3 - Conference contribution
SN - 978-3-319-49585-9
T3 - Lecture Notes in Computer Science
SP - 726
EP - 737
BT - ADMA 2016: Advanced Data Mining and Applications
A2 - Li, Jinyan
A2 - Li, Xue
A2 - Wang, Shuliang
A2 - Li, Jianxin
A2 - Sheng, Quan Z.
PB - Springer
T2 - 12th International Conference on Advanced Data Mining and Applications 2016
Y2 - 12 December 2016 through 15 December 2016
ER -