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KmL: k-means for longitudinal data

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Abstract

Cohort studies are becoming essential tools in epidemiological research. In these studies, measurements are not restricted to single variables but can be seen as trajectories. Statistical methods used to determine homogeneous patient trajectories can be separated into two families: model-based methods (like Proc Traj) and partitional clustering (non-parametric algorithms like k-means). KmL is a new implementation of k-means designed to work specifically on longitudinal data. It provides scope for dealing with missing values and runs the algorithm several times, varying the starting conditions and/or the number of clusters sought; its graphical interface helps the user to choose the appropriate number of clusters when the classic criterion is not efficient. To check KmL efficiency, we compare its performances to Proc Traj both on artificial and real data. The two techniques give very close clustering when trajectories follow polynomial curves. KmL gives much better results on non-polynomial trajectories.

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Authors and Affiliations

  1. Inserm, U669, Paris, France

    Christophe Genolini & Bruno Falissard

  2. Modal’X, Univ Paris Ouest Nanterre La Défense, Paris, France

    Christophe Genolini

  3. Univ Paris-Sud and Univ Paris Descartes, UMR-S0669, Paris, France

    Bruno Falissard

  4. Département de santé publique, AP-HP, Hôpital Paul Brousse, Villejuif, France

    Bruno Falissard

Authors
  1. Christophe Genolini
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  2. Bruno Falissard
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Correspondence to Christophe Genolini.

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Genolini, C., Falissard, B. KmL: k-means for longitudinal data. Comput Stat 25, 317–328 (2010). https://doi.org/10.1007/s00180-009-0178-4

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  • DOI: https://doi.org/10.1007/s00180-009-0178-4

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