Two of the new issues that functional data analysis is recently dealing with are the identification of local clusters, i.e. clusters defined only on a portion of the domain, and the discovery of functional motifs, i.e. typical “shapes” that may be repeated multiple times within each curve, or across several curves belonging to the same set. In this presentation, two new algorithms to solve these problems are presented.

They both are based on the H-score (or mean squared residue score), the evaluation score underlying the seminal biclustering algorithm by Cheng and Church, as well as many other subsequent biclustering methods, and they leverage ideas from multivariate biclustering and functional data analysis - especially curve alignment, functional clustering and biclustering.