Corpus ID: 231925308

HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis

@article{Shand2021HAWKSEC,
  title={HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis},
  author={Cameron Shand and R. Allmendinger and J. Handl and Andrew M. Webb and J. Keane},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.06940}
}
Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i) the elusiveness of a unique mathematical definition of this unsupervised learning approach and (ii) dependencies between the generating models or clustering criteria adopted by some clustering algorithms and indices for internal cluster validation. Consequently, there is no consensus regarding the best practice for rigorous benchmarking, and whether this is possible at all outside the context of a… Expand

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