Research of semi-supervised spectral clustering algorithm based on pairwise constraints |
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Authors: | Shifei Ding Hongjie Jia Liwen Zhang Fengxiang Jin |
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Affiliation: | 1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China 2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China 3. Geomatics College, Shandong University of Science and Technology, Qingdao, 266510, China
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Abstract: | Clustering is often considered as an unsupervised data analysis method, but making full use of the prior information in the process of clustering will significantly improve the performance of the clustering algorithm. Spectral clustering algorithm can well use the prior pairwise constraint information to cluster and has become a new hot spot of machine learning research in recent years. In this paper, we propose an effective clustering algorithm, called a semi-supervised spectral clustering algorithm based on pairwise constraints, in which the similarity matrix of data points is adjusted and optimized by pairwise constraints. The experiments on real-world data sets demonstrate the effectiveness of this algorithm. |
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