Optimizing the C Index Using a Canonical Genetic Algorithm

  • Thomas A. RunklerEmail author
  • James C. Bezdek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)


Clustering is an important family of unsupervised machine learning methods. Cluster validity indices are widely used to assess the quality of obtained clustering results. The C index is one of the most popular cluster validity indices. This paper shows that the C index can be used not only to validate but also to actually find clusters. This leads to difficult discrete optimization problems which can be approximately solved by a canonical genetic algorithm. Numerical experiments compare this novel approach to the well-known c-means and single linkage clustering algorithms. For all five considered popular real-world benchmark data sets the proposed method yields a better C index than any of the other (pure) clustering methods.


Cluster validity c-means Single linkage C index Genetic algorithm 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Siemens AG, Corporate TechnologyMunichGermany
  2. 2.University of MelbourneParkvilleAustralia

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