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 …

extension of an existing work on reducing dimensionality for model-based clustering based on Gaussian mixtures. Information on …

We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of <InlineEquation … can be projected onto the subspace and the resulting set of variables captures most of the clustering structure available …

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions …

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 …

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

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 … reduction of the curves and clustering, leading to functional representation of data depending on clusters. The last group …