CRAD: Clustering with Robust Autocuts and Depth

@article{Huang2017CRADCW,
  title={CRAD: Clustering with Robust Autocuts and Depth},
  author={X. Huang and Y. Gel},
  journal={2017 IEEE International Conference on Data Mining (ICDM)},
  year={2017},
  pages={925-930}
}
  • X. Huang, Y. Gel
  • Published 2017
  • Computer Science, Mathematics
  • 2017 IEEE International Conference on Data Mining (ICDM)
  • We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure is developed to select the optimal underlying parameter in the real-world clustering, when… CONTINUE READING
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