Abstract
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 language | English |
|---|---|
| Title of host publication | Advanced Data Mining and Applications |
| Subtitle of host publication | 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings |
| Editors | Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng |
| Publisher | Springer International Publishing |
| Pages | 726-737 |
| Number of pages | 12 |
| Volume | 10086 |
| ISBN (Electronic) | 978-3-319-49586-6 |
| ISBN (Print) | 978-3-319-49585-9 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | ADMA 2016 - Gold Coast, Australia Duration: 12 Dec 2016 → 15 Dec 2016 |
Publication series
| Name | Lecture Notes in Artificial Intelligence |
|---|---|
| Publisher | Springer |
Conference
| Conference | ADMA 2016 |
|---|---|
| Country | Australia |
| City | Gold Coast |
| Period | 12/12/16 → 15/12/16 |
Fingerprint
Keywords
- support vector clustering
- support vector ranking
- partitioning clustering
Cite this
Partitioning Clustering Based on Support Vector Ranking. / Peng, Qing ; Wang, Yan; Ou, Ge; Huang, Lan; Pang, Wei.
Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. ed. / Jinyan Li; Xue Li; Shuliang Wang; Jianxin Li; Quan Z. Sheng. Vol. 10086 Springer International Publishing, 2016. p. 726-737 (Lecture Notes in Artificial Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Partitioning Clustering Based on Support Vector Ranking
AU - Peng, Qing
AU - Wang, Yan
AU - Ou, Ge
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
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
VL - 10086
T3 - Lecture Notes in Artificial Intelligence
SP - 726
EP - 737
BT - Advanced Data Mining and Applications
A2 - Li, Jinyan
A2 - Li, Xue
A2 - Wang, Shuliang
A2 - Li, Jianxin
A2 - Sheng, Quan Z.
PB - Springer International Publishing
ER -