Automatic Graph Building Approach for Spectral Clustering

  • Andrés Eduardo Castro-Ospina
  • Andrés Marino Álvarez-Meza
  • César Germán Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Spectral clustering techniques have shown their capability to identify the data relationships using graph analysis, achieving better accuracy than traditional algorithms as k-means. Here, we propose a methodology to build automatically a graph representation over the input data for spectral clustering based approaches by taking into account the local and global sample structure. Regarding this, both the Euclidean and the geodesic distances are used to identify the main relationships between a given point and neighboring samples around it. Then, given the information about the local data structure, we estimate an affinity matrix by means of Gaussian kernel. Synthetic and real-world datasets are tested. Attained results show how our approach outperforms, in most of the cases, benchmark methods.

Keywords

Graph analysis kernel function spectral clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrés Eduardo Castro-Ospina
    • 1
  • Andrés Marino Álvarez-Meza
    • 1
  • César Germán Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

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