Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

Clustering techniques for functional data are reviewed. Four groups of clustering algorithms
for functional data are proposed. The first group consists of methods working directly on the …

The first model-based clustering algorithm for multivariate functional data is proposed. After
introducing multivariate functional principal components analysis (MFPCA), a parametric …

Bayesian compressive sensing (CS) is considered for signals and images that are sparse in
a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in …

A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-
centres FC approach accounts for both the means and the modes of variation differentials …

This article establishes a general formulation for Bayesian model-based clustering, in which
subset labels are exchangeable, and items are also exchangeable, possibly up to covariate …

Bike sharing systems (BSSs) have become a means of sustainable intermodal transport and
are now proposed in many cities worldwide. Most BSSs also provide open access to their …

We propose a hybrid approach for the modeling and the short-term forecasting of electricity
loads. Two building blocks of our approach are (1) modeling the overall trend and …

A new method for clustering functional data is proposed under the name Funclust. This
method relies on the approximation of the notion of probability density for functional random …