KmL: k-means for longitudinal data |
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Authors: | Christophe Genolini Bruno Falissard |
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Institution: | (1) Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada;(2) Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada |
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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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Keywords: | |
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