In this talk, the funBI family of algorithms is introduced. It is composed by two methods, funBI and funBIalign, and they both deal with functional data. funBI is a biclustering method for functional data and it aims at finding functional biclusters which are subsets of functions that exhibit similar behaviour across the same continuous subsets of the domain. Instead, funBIalign starts from the results of its predecessor broadening the definition of functional biclustering to deal with the problem of functional motifs discovery, i.e., the identification of typical “shapes” of defined lenght that may be repeated multiple times within each curve, or across several curves belonging to the same set. Both the algorithms leverage their main ideas from multivariate biclustering and functional data analysis, especially functional clustering and curves alignment.