Meta-learning Based Evolutionary Clustering Algorithm

  • Dmitry Tomp
  • Sergey MuravyovEmail author
  • Andrey Filchenkov
  • Vladimir Parfenov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)


In this work, we address the hard clustering problem. We present a new clustering algorithm based on evolutionary computation searching a best partition with respect to a given quality measure. We present 32 partition transformation that are used as mutation operators. The algorithm is a \((1+1)\) evolutionary strategy that selects a random mutation on each step from a subset of preselected mutation operators. Such selection is performed with a classifier trained to predict usefulness of each mutation for a given dataset. Comparison with state-of-the-art approach for automated clustering algorithm and hyperparameter selection shows the superiority of the proposed algorithm.


Clustering Evolutionary clustering Meta-learning Evolutionary computation 



The authors would like to thank Maxim Buzdalov for useful comments. The research was financially supported by The Russian Science Foundation, Agreement 17-71-30029.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dmitry Tomp
    • 1
    • 2
  • Sergey Muravyov
    • 1
    • 2
    Email author
  • Andrey Filchenkov
    • 1
    • 2
  • Vladimir Parfenov
    • 2
  1. 1.Machine Learning LabITMO UniversitySt. PetersburgRussia
  2. 2.Information Technologies and Programming FacultyITMO UniversitySt. PetersburgRussia

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