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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.05.2013 | Original Article

Research of semi-supervised spectral clustering based on constraints expansion

verfasst von: Shifei Ding, Bingjuan Qi, Hongjie Jia, Hong Zhu, Liwen Zhang

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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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.
Vorheriger Artikel Using competitive layer model implemented by Lotka–Volterra recurrent neural networks for detecting brain activated regions from fMRI data
Nächster Artikel Evolutionary fuzzy clustering and functional modular neural network-based human recognition

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Metadaten
Titel
Research of semi-supervised spectral clustering based on constraints expansion
verfasst von
Shifei Ding
Bingjuan Qi
Hongjie Jia
Hong Zhu
Liwen Zhang
Publikationsdatum
01.05.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0911-8

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