FunCC: a new bi-clustering algorithm for functional data with misalignment
Saturday 7th November 2020
Galvani, M.; Torti, A.; Menafoglio, A.; Vantini S.
The problem of bi-clustering functional data, which has recently been addressed in literature, is considered. A definition of ideal functional bi-cluster is given and a novel bi-clustering method, called Functional Cheng and Church (FunCC), is developed. The introduced algorithm searches for non-overlapping and non-exhaustive bi-clusters in a set of functions which are naturally ordered in matrix structure through a non-parametric deterministic iterative procedure. Moreover, the possible misalignment of the data, which is a common problem when dealing with functions, is taken into account. Hence, the FunCC algorithm is extended obtaining a model able to jointly bi-cluster and align curves. Different simulation studies are performed to show the potential of the introduced method and to compare it with state-of-the-art methods. The model is also applied on a real case study allowing to discover the spatio-temporal patterns of a bike-sharing system infrastructure.