一种基于成对约束的谱聚类算法

Abstract

Abstract: Clustering has been traditionally viewed as an unsupervised method.Inreal world applications,it has been demonstrated that constraints can improve clustering performance.In this paper,a new semi-supervised spectral clusteringmethod based on pairwise constraints is proposed.The similar matrix is adjusted by the pairwise constraints,and then optimized.Combined with spectral clustering,an effective clustering algorithm can be obtained.The experiment shows thatthe algorithm has good clustering effect.

Key words: spectral clustering, prior knowledge, pair wise constraints, semi-supervised clustering

CLC Number:

TP301.6

ZHANG Li-wen, DING Shi-fei, XU Xin-zheng, ZHU Hong, XU Li. Semi-Supervised Spectral Clustering Algorithms Based on Pair Wise Constraints[J].Journal of Guangxi Normal University(Natural Science Edition), 2010, 28(4): 38-41.

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Semi-Supervised Spectral Clustering Algorithms Based on Pair Wise Constraints

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