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

Wenlin Dai, Stavros Athanasiadis and Tomas Mrkvicka

A chapter in Computational Statistics and Applications from IntechOpen

Abstract: 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 with 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.

Keywords: depth; insurance; intrinsic graphical interpretation; robustness; statistical rankings (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:242760

DOI: 10.5772/intechopen.100124

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