Pattern Analysis and Applications

, Volume 20, Issue 2, pp 495–506 | Cite as

Spectral clustering based on similarity and dissimilarity criterion

  • Bangjun WangEmail author
  • Li Zhang
  • Caili Wu
  • Fan-zhang Li
  • Zhao Zhang
Theoretical Advances


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.


Spectral clustering Normalized cut Similarity criterion Dissimilarity criterion 


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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Bangjun Wang
    • 1
    • 2
    Email author
  • Li Zhang
    • 2
  • Caili Wu
    • 2
  • Fan-zhang Li
    • 2
  • Zhao Zhang
    • 2
  1. 1.Beijing Jiaotong UniversityBeijingChina
  2. 2.Soochow UniversitySuzhouChina

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