Two Novel Kernel-Based Semi-Supervised Clustering Methods by Seeding

Semi-supervised clustering takes advantage of a small amount of labeled data to bring a great benefit to the clustering of unlabeled data. Based on a novel kernel method for clustering using one-class support vector machine, this paper presents two novel kernel-based semi-supervised clustering methods inspired by two semi-supervised variants of the k-means clustering algorithm by seeding respectively. To investigate the effectiveness of our approaches, experiments are done on three real datasets. Experimental results show that the proposed methods can improve the clustering performance significantly compared to other unsupervised and semi-supervised clustering algorithms.