Two-phase support vector clustering for multi-relational data mining

A novel two-phase support vector clustering (TPSVC) algorithm is proposed in this paper, which is implemented in multi-relational data mining (MRDM). Based on the designed kernel which is incorporated with MRDM environment, TPSVC provides an appreciate description of cluster contours using support vectors at the first step and then a support vector machine (SVM) classification procedure is employed to further extract the information of cluster central zones. The algorithm does the cluster assignment according to desired definition of affinity without suffering the expensive operations of adjacent matrix computation used in traditional support vector clustering (SVC). Experimental results indicate that the designed kernel can capture the features of relational schema and TPSVC is of fine clustering performance