Hierarchical Similarity Measure for Spectral Clustering


  • Lucas Siviero Sibemberg
  • Luiz Emilio Allem
  • Carlos Hoppen




Data Science, Clustering, Spectral Graph Theory, Spectral Clustering, Similarity Measure.


In this paper we propose a novel similarity measure for spectral clustering that incorporates a hierarchical component. The main advantage of this measure is that it produces an algorithm that does not depend on any scaling parameter, making it very easy to apply. Our experiments showed that our algorithm performs better than other spectral clustering methods on synthetic data sets with complex shape and multiple scales.


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Biografia do Autor

Lucas Siviero Sibemberg

IME/UFRGS, Porto Alegre, RS

Luiz Emilio Allem

IME/UFRGS, Porto Alegre, RS

Carlos Hoppen

IME/UFRGS, Porto Alegre, RS


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