Density-Based Clustering Based on Hierarchical Density Estimates (original) (raw)

Abstract

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.

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Authors and Affiliations

  1. Dept. of Computing Science, University of Alberta, Edmonton, AB, Canada
    Ricardo J. G. B. Campello, Davoud Moulavi & Joerg Sander

Authors

  1. Ricardo J. G. B. Campello
  2. Davoud Moulavi
  3. Joerg Sander

Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada
    Jian Pei
  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
    Vincent S. Tseng
  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia
    Longbing Cao & Guandong Xu &
  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan
    Hiroshi Motoda

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Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2\_14

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