Gareth James: Clustering of sparsely sampled functional data

Abstract

  I develop a flexible procedure for clustering functional
  data. The technique can be used on all types of curve data but is
  particularly useful when individuals are observed at a sparse set of
  time points. In addition to producing final cluster assignments, the
  procedure generates predictions and confidence intervals for missing
  portions of curves.  The approach also provides many useful tools for
  evaluating the resulting clusters. Clustering can be assessed
  visually via low dimensional representations of the curves, and the
  regions of greatest separation between clusters can be determined using
  a discriminant function.