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arXiv · 1004.0456

Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

Georges Hébrail·Bernard Hugueney·Yves Lechevallier·Fabrice Rossi
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Abstract

We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into $K$ clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, $P$, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets.

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Georges Hébrail, Bernard Hugueney, Yves Lechevallier, Fabrice Rossi. 2010-04-03. Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation. https://doi.org/10.1016/j.neucom.2009.11.022

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