  We consider the problem of clustering misaligned curves. According to our
similarity measure, two curves are considered similar if they have the same
shape after being aligned, and the warping function does not differ from the
identity function very much.
  A clustering method is proposed, which updates curves so that similar curves
become more similar, and then combines curves that are similar enough to form
clusters. The proposed method needs to be used together with a clustering index
and a set of combination thresholds.
  Simulation results are presented to demonstrate the performance of this
approach under different parameter settings and clustering indexes. Two real
data applications are included.
