Robust clustering of functional directional data

Pedro C. Álvarez-Esteban () and Luis A. García-Escudero ()
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Pedro C. Álvarez-Esteban: IMUVA, Universidad de Valladolid
Luis A. García-Escudero: IMUVA, Universidad de Valladolid

Advances in Data Analysis and Classification, 2022, vol. 16, issue 1, No 8, 199 pages

Abstract: Abstract A robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem where it is applied to cluster aircraft trajectories.

Keywords: Cluster analysis; Robustness; Functional data analysis; Directional data; Warping; 62H30; 62H11; 62G35 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11634-021-00482-3

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