  Nowadays, more and more problems are dealing with data with one infinite
continuous dimension: functional data. In this paper, we introduce the funLOCI
algorithm which allows to identify functional local clusters or functional
loci, i.e., subsets/groups of functions exhibiting similar behaviour across the
same continuous subset of the domain. The definition of functional local
clusters leverages ideas from multivariate and functional clustering and
biclustering and it is based on an additive model which takes into account the
shape of the curves. funLOCI is a three-step algorithm based on divisive
hierarchical clustering. The use of dendrograms allows to visualize and to
guide the searching procedure and the cutting thresholds selection. To deal
with the large quantity of local clusters, an extra step is implemented to
reduce the number of results to the minimum.
