TITLE

Research of semi-supervised spectral clustering based on constraints expansion

AUTHOR(S)
Ding, Shifei; Qi, Bingjuan; Jia, Hongjie; Zhu, Hong; Zhang, Liwen
PUB. DATE
May 2013
SOURCE
Neural Computing & Applications;May2013 Supplement, Vol. 22, p405
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Semi-supervised learning has become one of the hotspots in the field of machine learning in recent years. It is successfully applied in clustering and improves the clustering performance. This paper proposes a new clustering algorithm, called semi-supervised spectral clustering based on constraints expansion (SSCCE). This algorithm expands the known constraints set, changes the similarity relation of the sample points through the density-sensitive path distance, and then combines with semi-supervised spectral clustering to cluster. The experimental results prove that SSCCE algorithm has good clustering effect.
ACCESSION #
87661110

 

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