A two-stage ensemble clustering algorithm for functional data that uses Gaussian-process random projections for initial grouping followed by covariance-based refinement and cost-based selection.
A k-means procedure based on a mahalanobis type distance for clustering multivariate functional data.Statistical Methods & Appli- cations, 28:301–322
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Two-stage Ensemble Clustering of Functional Data Using Random Projections
A two-stage ensemble clustering algorithm for functional data that uses Gaussian-process random projections for initial grouping followed by covariance-based refinement and cost-based selection.