RT info:eu-repo/semantics/article T1 Robust clustering of functional directional data A1 Álvarez Esteban, Pedro César A1 García Escudero, Luis Ángel K1 Cluster analysis K1 Robustness K1 Functional data analysis K1 Directional data K1 Warping K1 12 Matemáticas AB 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. PB Springer SN 1862-5347 YR 2021 FD 2021 LK https://uvadoc.uva.es/handle/10324/53351 UL https://uvadoc.uva.es/handle/10324/53351 LA eng NO Advances in Data Analysis and Classification, 2021, vol. 16, n. 1, p. 181-199 NO Producción Científica DS UVaDOC RD 22-dic-2024