The novel seeding-based semi-supervised fuzzy clustering algorithm inspired by diffusion processes

Authors:
Lei Gu
Affiliations:
Guangxi Key Laboratory of Wireless Wideband Communication & Signal Processing, Guilin, China,School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanj ...
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

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.