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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Acknowledgments
This work is supported by the National Basic Research Program of China (No. 2013CB329502), the National Natural Science Foundation of China (No. 41074003) and the Opening Foundation of Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No. IIP2010-1).
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Ding, S., Jia, H., Zhang, L. et al. Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Comput & Applic 24, 211–219 (2014). https://doi.org/10.1007/s00521-012-1207-8
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DOI: https://doi.org/10.1007/s00521-012-1207-8