Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

Rossi, Fabrice; Lechevallier, Yves; Hugueney, Bernard; Hébrail, Georges

Rossi, Fabrice; Lechevallier, Yves; Hugueney, Bernard; Hébrail, Georges (2010), Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation, Neurocomputing, 73, 7-9, p. 1125-1141. http://dx.doi.org/10.1016/j.neucom.2009.11.022

Type

Article accepté pour publication ou publié

External document link

http://fr.arxiv.org/abs/1004.0456

Date

2010

Journal name

Neurocomputing

Volume

Number

7-9

Publisher

Elsevier

Pages

1125-1141

Abstract (EN)

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.

Subjects / Keywords

Dynamic programming; Segmentation; Clustering; Exploratory analysis; Multiple time series; Functional Data