Functional clustering and identifying substructures of longitudinal data
A functional clustering (FC) method, "k"-centres FC, for longitudinal data is proposed. The "k"-centres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a non-parametric random-effect model of the truncated Karhunen-Loève expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We show that, under the identifiability conditions derived, the "k"-centres FC method proposed can greatly improve cluster quality as compared with conventional clustering algorithms. Moreover, by exploring the mean and covariance functions of each cluster, the"k"-centres FC method provides an additional insight into cluster structures which facilitates functional cluster analysis. Practical performance of the "k"-centres FC method is demonstrated through simulation studies and data applications including growth curve and gene expression profile data. Copyright 2007 Royal Statistical Society.
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Volume (Year): 69 (2007)
Issue (Month): 4 ()
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