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Advances in Knowledge Discovery and Management pp 141–155Cite as

Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 665))

Abstract

We introduce a measure of ultrametricity for dissimilarity spaces and examine transformations of dissimilarities that impact this measure. Then, we study the influence of ultrametricity on the behavior of two classes of data mining algorithms (kNN classification and PAM clustering) applied on dissimilarity spaces. We show that there is an inverse variation between ultrametricity and performance of classifiers. For clustering, increased ultrametricity generate clusterings with better separation. Lowering ultrametricity produces more compact clusters.

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Acknowledgments

The authors wish to thank the referees for the careful reading of the paper and for their suggestions and observations.

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Authors and Affiliations

  1. University of Massachusetts Boston, Boston, USA

    Dan A. Simovici, Rosanne Vetro & Kaixun Hua

Authors
  1. Dan A. Simovici
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  2. Rosanne Vetro
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  3. Kaixun Hua
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Corresponding author

Correspondence to Dan A. Simovici .

Editor information

Editors and Affiliations

  1. Polytech Nantes, University of Nantes, Nantes Cedex 3, France

    Fabrice Guillet

  2. University of Bordeaux, Talence Cedex, France

    Bruno Pinaud

  3. Polytechnics Graduate School, University of Tours, Tours, France

    Gilles Venturini

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Simovici, D.A., Vetro, R., Hua, K. (2017). Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining. In: Guillet, F., Pinaud, B., Venturini, G. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 665. Springer, Cham. https://doi.org/10.1007/978-3-319-45763-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-45763-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45762-8

  • Online ISBN: 978-3-319-45763-5

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