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Computational Statistics [Working Title]
Clustering is an essential task in functional data analysis. In this study, we propose a framework for a clustering procedure based on functional rankings or depth. Our methods naturally combine various types of between-cluster variation equally, which caters to various discriminative sources of functional data; for example, they combine raw data with transformed data or various components of multivariate functional data with their covariance. Our methods also enhance the clustering resultsdoi:10.5772/intechopen.100124 fatcat:rui6k3qwpjg23b3lheiqkqyafa