Spectral clustering based on similarity and dissimilarity criterion

Spectral clustering based on similarity and dissimilarity criterion

The clustering assumption is to maximize the within-cluster similarity and simultaneously to minimize the between-cluster similarity for a given unlabeled dataset. This paper deals with a new spectral clustering algorithm based on a similarity and dissimilarity criterion by incorporating a dissimila...

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Veröffentlicht in:Pattern analysis and applications : PAA 2017-05, Vol.20 (2), p.495-506
Hauptverfasser: Wang, Bangjun, Zhang, Li, Wu, Caili, Li, Fan-zhang, Zhang, Zhao
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Sprache:eng
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Clustering
Clusters
Computer Science
Criteria
Pattern Recognition
Similarity
Spectra
Theoretical Advances
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container_title Pattern analysis and applications : PAA
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creator Wang, Bangjun
Zhang, Li
Wu, Caili
Li, Fan-zhang
Zhang, Zhao
description The clustering assumption is to maximize the within-cluster similarity and simultaneously to minimize the between-cluster similarity for a given unlabeled dataset. This paper deals with a new spectral clustering algorithm based on a similarity and dissimilarity criterion by incorporating a dissimilarity criterion into the normalized cut criterion. The within-cluster similarity and the between-cluster dissimilarity can be enhanced to result in good clustering performance. Experimental results on toy and real-world datasets show that the new spectral clustering algorithm has a promising performance.
doi_str_mv 10.1007/s10044-015-0515-x
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subjects Clustering
Clusters
Computer Science
Criteria
Pattern Recognition
Similarity
Spectra
Theoretical Advances
title Spectral clustering based on similarity and dissimilarity criterion
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