Applying More Initial Groups to the Semi-Supervised Clustering Based on one-Class Support Vector Machines

Abstract

The traditional semi-supervised clustering based on one-class support vector machines used some labeled data called seeds for the clustering initialization. These seeds were partitioned into several initial groups according to their labels and the number of initial groups was equal to the number of clusters. However, the traditional semi-supervised clustering based on one-class support vector machines is sensitive to the initial groups and often obtained the local optimal solutions. In this paper, more initial groups produced by seeds are applied to the traditional semi-supervised clustering based on one-class support vector machines to get more local optimal solutions and the proposed algorithm can combine multiple local optimal solutions to obtain the better clustering performance at last. To investigate the effectiveness of our approach, experiments are done on two real datasets. Experimental results show that the presented method can improve the clustering accuracies compared to the traditional algorithm.