Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are … defined by filtering methods which first approximate the curves into a finite basis of functions and second perform clustering … using the basis expansion coefficients. The third groups is composed of methods which perform simultaneously dimensionality …

functional objects and also find an optimal subspace for clustering, simultaneously. The method is based on the k-means criterion … for functional data and seeks the subspace that is maximally informative about the clustering structure in the data. An …

solutions to this problem and are effective in higher dimensions. We use mixture model-based clustering applications to …

such as clustering. Though a binary integer linear programming formulation has been known for years, one needs to deal with … experiments. We modify the conventional subgradient method in order to manage the high dimensionality of the Lagrangian …

In this article, we propose a novel Bayesian nonparametric clustering algorithm based on a Dirichlet process mixture of …