Clustering validity assessment: finding the optimal partitioning of a data set

@article{Halkidi2001ClusteringVA,
  title={Clustering validity assessment: finding the optimal partitioning of a data set},
  author={M. Halkidi and M. Vazirgiannis},
  journal={Proceedings 2001 IEEE International Conference on Data Mining},
  year={2001},
  pages={187-194}
}
  • M. Halkidi, M. Vazirgiannis
  • Published 2001
  • Computer Science
  • Proceedings 2001 IEEE International Conference on Data Mining
  • Clustering is a mostly unsupervised procedure and the majority of clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. [...] Key Method We define a validity index, S Dbw, based on well-defined clustering criteria enabling the selection of optimal input parameter values for a clustering algorithm that result in the best partitioning of a data set. We evaluate the reliability of our index both theoretically and experimentally, considering three representative…Expand Abstract
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