Density-Based Clustering Based on Hierarchical Density Estimates

  • Ricardo J. G. B. Campello
  • Davoud Moulavi
  • Joerg Sander
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.


Minimum Span Tree Cluster Tree Density Threshold Hierarchical Cluster Method Core Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ricardo J. G. B. Campello
    • 1
  • Davoud Moulavi
    • 1
  • Joerg Sander
    • 1
  1. 1.Dept. of Computing ScienceUniversity of AlbertaEdmontonCanada

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