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Simplicial similarity and its application to hierarchical clustering

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  • Ángel López

    ()

  • Juan Romo

    ()

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    Abstract

    In the present document, an extension of the statistical depth notion is introduced with the aim to allow for measuring proximities between pairs of points. In particular, we will extend the simplicial depth function, which measures how central is a point by using random simplices (triangles in the two-dimensional space). The paper is structured as follows: In first place, there is a brief introduction to statistical depth functions. Next, the simplicial similarity function will be defined and its properties studied. Finally, we will present a few graphical examples in order to show its behavior with symmetric and asymmetric distributions, and apply the function to hierarchical clustering.

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/8883/5/ws102915.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws102915.

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    Date of creation: Jun 2010
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    Handle: RePEc:cte:wsrepe:ws102915

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    Related research

    Keywords: Statistical depth; Similarity measures; Hierarchical clustering;

    This paper has been announced in the following NEP Reports:

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