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On the Spectral Clustering for Dynamic Data

  • D. H. Peluffo-Ordóñez
  • J. C. Alvarado-Pérez
  • A. E. Castro-Ospina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)

Abstract

Spectral clustering has shown to be a powerful technique for grouping and/or rank data as well as a proper alternative for unlabeled problems. Particularly, it is a suitable alternative when dealing with pattern recognition problems involving highly hardly separable classes. Due to its versatility, applicability and feasibility, this clustering technique results appealing for many applications. Nevertheless, conventional spectral clustering approaches lack the ability to process dynamic or time-varying data. Within a spectral framework, this work presents an overview of clustering techniques as well as their extensions to dynamic data analysis.

Keywords

Dynamic data Kernels Spectral clustering 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • D. H. Peluffo-Ordóñez
    • 1
  • J. C. Alvarado-Pérez
    • 2
  • A. E. Castro-Ospina
    • 3
  1. 1.Universidad Cooperativa de Colombia – PastoPastoColombia
  2. 2.Universidad de SalamancaSalamancaSpain
  3. 3.Research Center of the Instituto Tecnológico MetropolitanoMedellínColombia

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