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)


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.


Spectral Decomposition Geometric Object Iterative Version Integrate Circuit Design Dissimilarity Coefficient 
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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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