Research of semi-supervised spectral clustering based on constraints expansion

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.

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