Robust clustering of functional directional data

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 tell us which appears be most outlying curves. feasible algorithm proposed its practical implementation justified by some theoretical properties. “warping” also introduced allows including controlled time warping in that procedure detect typical “templates”. methodology illustrated a real analysis problem where it applied cluster aircraft trajectories.