Research of semi-supervised spectral clustering algorithm based on pairwise constraints

• Published in: Neural computing & applications Vol. 24; no. 1; pp. 211 - 219
• Main Authors: Ding, Shifei, Jia, Hongjie, Zhang, Liwen, Jin, Fengxiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2014; Springer
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Exact sciences and technology; Image Processing and Computer Vision; Memory organisation. Data processing; Original Article; Probability and Statistics in Computer Science; Software; Pairwise constraints; Prior information; Spectral clustering; Semi-supervised clustering; Cluster analysis; Data analysis; Spectral method; Hot spot; Cluster; Data mining; Unsupervised learning; Constrained optimization; Semi-supervised learning; Selection criterion; Artificial intelligence
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1207-8

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.