A New Functional Clustering Method with Combined Dissimilarity Sources and Graphical Interpretation [chapter]

Wenlin Dai, Stavros Athanasiadis, Tomáš Mrkvička
2021 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 results
more » ... a visualization tool that allows intrinsic graphical interpretation. Finally, our methods are model-free and nonparametric and hence are robust to heavy-tailed distribution or potential outliers. The implementation and performance of the proposed methods are illustrated with a simulation study and applied to three real-world applications.
doi:10.5772/intechopen.100124 fatcat:rui6k3qwpjg23b3lheiqkqyafa