  We introduce the functional mean-shift algorithm, an iterative algorithm for
estimating the local modes of a surrogate density from functional data. We show
that the algorithm can be used for cluster analysis of functional data. We
propose a test based on the bootstrap for the significance of the estimated
local modes of the surrogate density. We present two applications of our
methodology. In the first application, we demonstrate how the functional
mean-shift algorithm can be used to perform spike sorting, i.e. cluster neural
activity curves. In the second application, we use the functional mean-shift
algorithm to distinguish between original and fake signatures.
