Mean shift-based clustering for misaligned functional data

Andrew Welbaum and Wanli Qiao

Computational Statistics & Data Analysis, 2025, vol. 206, issue C

Abstract: Misalignment often occurs in functional data and can severely impact their clustering results. A clustering algorithm for misaligned functional data is developed, by adapting the original mean shift algorithm in the Euclidean space. This mean shift algorithm is applied to the quotient space of the orbits of the square root velocity functions induced by the misaligned functional data, in which the elastic distance is equipped. Convergence properties of this algorithm are studied. The efficacy of the algorithm is demonstrated through simulations and various real data applications.

Keywords: Clustering; Functional data; Mean shift; Gradient ascent; Misalignment; Elastic distance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:206:y:2025:i:c:s0167947324001919

DOI: 10.1016/j.csda.2024.108107

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