Name
Abstract | ||
|---|---|---|
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. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.csda.2021.107219 | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
Keywords | DocType | Volume |
Bi-clustering, Clustering, Functional data, Curve alignment, Mobility, Bike Sharing System | Journal | 160 |
ISSN | Citations | PageRank |
0167-9473 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Galvani | 1 | 0 | 0.68 |
| Agostino Torti | 2 | 0 | 0.34 |
| Alessandra Menafoglio | 3 | 17 | 5.25 |
| Simone Vantini | 4 | 0 | 0.34 |