An incremental affinity propagation algorithm and its applications for text clustering

Authors:
X. H. Shi;R. C. Guan;L. P. Wang;Z. L. Pei;Y. C. Liang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;Department of Computer Science, Inner Mongolia University for the Nationalities, Tongliao, China and College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Affinity propagation is an impressive clustering algorithm which was published in Science, 2007. However, the original algorithm couldn't cope with part known data directly. Focusing on this issue, a semi-supervised scheme called incremental affinity propagation clustering is proposed in the paper. In the scheme, the pre-known information is represented by adjusting similarity matrix. Moreover, an incremental study is applied to amplify the prior knowledge. To examine the effectiveness of the method, we concentrate it to text clustering problem and describe the specific method accordingly. The method is applied to the benchmark data set Reuters-21578. Numerical results show that the proposed method performs very well on the data set and has most advantages over two other commonly used clustering methods.