A Quality-Driven Ensemble Approach to Automatic Model Selection in Clustering

  • Raffaella Rosasco
  • Hassan Mahmoud
  • Stefano Rovetta
  • Francesco Masulli
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)


A fundamental limitation of the data clustering task is that it has an inherent, ill-defined model selection problem: the choice of a clustering technique also implies some a-priori decision on cluster geometry. In this work we explore the combined use of two different clustering paradigms and their combination by means of an ensemble technique. Mixing coefficients are computed on the basis of partition quality, so that the ensemble is automatically tuned so as to give more weight to the best-performing (in terms of the selected quality indices) clustering method.


Central clustering Fuzzy clustering Possibilistic c-Means Spectral clustering Clustering quality Ensemble clustering 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raffaella Rosasco
    • 1
  • Hassan Mahmoud
    • 1
  • Stefano Rovetta
    • 1
  • Francesco Masulli
    • 1
    • 2
  1. 1.DIBRISUniversity of GenoaGenoaItaly
  2. 2.Center for BiotechnologyTemple UniversityPhiladelphiaUSA

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