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Neural Computing and Applications

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01-01-2014 | Original Article

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

Authors: Shifei Ding, Hongjie Jia, Liwen Zhang, Fengxiang Jin

Published in: Neural Computing and Applications | Issue 1/2014

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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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Title: Research of semi-supervised spectral clustering algorithm based on pairwise constraints
Authors: Shifei Ding
Hongjie Jia
Liwen Zhang
Fengxiang Jin
Publication date: 01-01-2014
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 1/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1207-8

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