Quadratic Problem Formulation with Linear Constraints for Normalized Cut Clustering

  • D. H. Peluffo-Ordóñez
  • C. Castro-Hoyos
  • Carlos D. Acosta-Medina
  • Germán Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)


This work describes a novel quadratic formulation for solving the normalized cuts-based clustering problem as an alternative to spectral clustering approaches. Such formulation is done by establishing simple and suitable constraints, which are further relaxed in order to write a quadratic functional with linear constraints. As a meaningful result of this work, we accomplish a deterministic solution instead of using a heuristic search. Our method reaches comparable performance against conventional spectral methods, but spending significantly lower processing time.


Linear Constraint Spectral Cluster Quadratic Problem Quadratic Programming Algorithm Lower Processing Time 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • D. H. Peluffo-Ordóñez
    • 1
  • C. Castro-Hoyos
    • 2
  • Carlos D. Acosta-Medina
    • 2
  • Germán Castellanos-Domínguez
    • 2
  1. 1.Univerisdad Cooperativa de Colombia – PastoPastoColombia
  2. 2.Universidad Nacional de Colombia – ManizalesManizalesColombia

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