Abstract
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.
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 |
Keywords
- support vector clustering
- support vector ranking
- partitioning clustering
Fingerprint Dive into the research topics of 'Partitioning Clustering Based on Support Vector Ranking'. Together they form a unique fingerprint.
Cite this
Peng, Q., Wang, Y., Ou, G., Huang, L., & Pang, W. (2016). Partitioning Clustering Based on Support Vector Ranking. In J. Li, X. Li, S. Wang, J. Li, & Q. Z. Sheng (Eds.), Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings (Vol. 10086, pp. 726-737). (Lecture Notes in Artificial Intelligence). Springer International Publishing. https://doi.org/10.1007/978-3-319-49586-6_52