The Novel Seeding-Based Semi-supervised Fuzzy Clustering Algorithm Inspired by Diffusion Processes

摘要

Semi-supervised clustering can take advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper uses the seeding-based semi-supervised idea for a fuzzy clustering method inspired by diffusion processes, which has been presented recently. To investigate the effectiveness of our approach, experiments are done on three UCI real data sets. Experimental results show that the proposed algorithm can improve the clustering performance significantly compared to other semi-supervised clustering approaches.