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Numerical Comparisons of two Spectral Decompositions for Vertex Clustering

  • P. Kuntz
  • F. Henaux
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We study multi-way partitioning algorithms of a hypergraph which are based on its prior transformation into a geometric object by constructing a one-to-one mapping between the vertex set and a point set in a Euclidean space. The coordinates of the points are generated by a spectral decomposition of a positive semi-definite matrix. Here, we compare the decomposition of the discrete Laplacian of a graph associated with the hypergraph to that of the Torgerson matrix associated with a dissimilarity coefficient. Numerical results are presented on standard test cases of large sizes from the integrated circuit design literature.

Keywords

Spectral Decomposition Geometric Object Iterative Version Integrate Circuit Design Dissimilarity Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • P. Kuntz
    • 1
  • F. Henaux
    • 2
  1. 1.IRIN-IRESTEFrance
  2. 2.ENSTParisFrance

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