A Novel SVC Method Based on K-means

In this paper, a novel support vector clustering algorithm based on k-means (SVC-KM) is presented not only improve the SVC running speed, but also overcome the weaknesses of k-means algorithm. Firstly, SVC algorithm was employed to identify some samples as outliers and some others as intra-cluster points, so that it removed noise and extracted samples near to the cluster core. Then the proposed method used Minimum Spanning Tree Pruning (MSTP) strategy on those intra-cluster points to initialize the number of clusters and clustering centroids which would be the parameters for k-means algorithm. Finally, it run k-means algorithm on subset without outliers to obtain the clustering result and assigned outliers into the nearest cluster. Applying the proposed algorithm to several test datasets, the experiments results compared with initial SVC algorithm and classical k-means strongly validate the efficiency and feasibility of SVC-KM.