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Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

  • María Eugenia Curi
  • Lucía Carozzi
  • Renzo Massobrio
  • Sergio NesmachnowEmail author
  • Grégoire Danoy
  • Marek Ostaszewski
  • Pascal Bouvry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10835)

Abstract

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson’s disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information.

Keywords

Clustering Biomedical information Multiobjective 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • María Eugenia Curi
    • 1
  • Lucía Carozzi
    • 1
  • Renzo Massobrio
    • 1
  • Sergio Nesmachnow
    • 1
    Email author
  • Grégoire Danoy
    • 2
  • Marek Ostaszewski
    • 3
  • Pascal Bouvry
    • 2
  1. 1.Universidad de la RepúblicaMontevideoUruguay
  2. 2.FSTC/CSC-ILIASUniversity of LuxembourgLuxembourg CityLuxembourg
  3. 3.LCSBUniversity of LuxembourgLuxembourg CityLuxembourg

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