An empirical comparison and characterisation of nine popular clustering methods

  title={An empirical comparison and characterisation of nine popular clustering methods},
  author={Christian Hennig},
  journal={Advances in Data Analysis and Classification},
  • C. Hennig
  • Published 6 February 2021
  • Computer Science
  • Advances in Data Analysis and Classification
Nine popular clustering methods are applied to 42 real data sets. The aim is to give a detailed characterisation of the methods by means of several cluster validation indexes that measure various individual aspects of the resulting clusters such as small within-cluster distances, separation of clusters, closeness to a Gaussian distribution etc. as introduced in Hennig (in: Data analysis and applications 1: clustering and regression, modeling—estimating, forecasting and data mining, ISTE Ltd… 
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  • 2019
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