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Hausdorff clustering

Author

Listed:
  • N. Basalto
  • R. Bellotti
  • F. De Carlo
  • P. Facchi
  • E. Pantaleo
  • S. Pascazio

Abstract

A clustering algorithm based on the Hausdorff distance is introduced and compared to the single and complete linkage. The three clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features confronted. The Hausdorff linkage relies of firm mathematical grounds and turns out to be very effective when one has to discriminate among complex structures.

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  • N. Basalto & R. Bellotti & F. De Carlo & P. Facchi & E. Pantaleo & S. Pascazio, 2008. "Hausdorff clustering," Papers 0801.0748, arXiv.org.
    • Handle: RePEc:arx:papers:0801.0748
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    File URL: http://arxiv.org/pdf/0801.0748
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    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Achilleas Anastasiou & Peter Hatzopoulos & Alex Karagrigoriou & George Mavridoglou, 2021. "Causality Distance Measures for Multivariate Time Series with Applications," Mathematics, MDPI, vol. 9(21), pages 1-15, October.

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