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.
|Name||Lecture Notes in Computer Science|
|Conference||12th International Conference on Advanced Data Mining and Applications 2016|
|Abbreviated title||ADMA 2016|
|Period||12/12/16 → 15/12/16|
- support vector clustering
- support vector ranking
- partitioning clustering