Abstract

Functional data that are not perfectly aligned in the sense of not showing peaks and valleys at the precise same locations possess phase variation. This is commonly addressed by preprocessing the data via a warping procedure. As opposed to treating phase variation as a nuisance effect, it is advantageous to recognize it as a possible important source of information for clustering. It is illustrated how results from a multiresolution warping procedure can be used for clustering. This approach allows us to address detailed questions to find local clusters that differ in phase, or clusters that differ in amplitude, or both simultaneously. (C) 2012 Elsevier B.V. All rights reserved.