Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

@article{Campello2015HierarchicalDE,
  title={Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection},
  author={Ricardo J. G. B. Campello and Davoud Moulavi and Arthur Zimek and J{\"o}rg Sander},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  year={2015},
  volume={10},
  pages={1 - 51}
}
An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy… 

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