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Functional clustering on a circle using von Mises mixtures. (English) Zbl 1473.62402

Summary: This paper addresses the question of clustering density curves around a unit circle by approximating each such curve by a mixture of an appropriate number of von Mises distributions. This is done first by defining a distance between any two such curves either via \(L^2\) or a symmetrized Kullback-Leibler divergence. We show that both these measures yield similar results. After demonstrating via simulations that the proposed clustering methods work successfully, they are applied on an illustrative sample of Optical Coherence Tomography data.

MSC:

62R10 Functional data analysis
62H11 Directional data; spatial statistics

Software:

clue; circular; CircStats
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Full Text: DOI Link

References:

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