Unfolding preprocessing for meaningful time series clustering

Authors:
Geoffroy Simon;John A. Lee;Michel Verleysen
Affiliations:
Machine Learning Group - DICE, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;Machine Learning Group - DICE, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;Machine Learning Group - DICE, Université Catholique de Louvain, Louvain-la-Neuve, Belgium and SAMOS-MATISSE, Université Paris I - Panthéon Sorbonne, Paris Cedex, France
Venue:
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Year:
2006

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Abstract

Clustering methods are commonly applied to time series, either as a preprocessing stage for other methods or in their own right. In this paper it is explained why time series clustering may sometimes be considered as meaningless. This problematic situation is illustrated for various raw time series. The unfolding preprocessing methodology is then introduced. The usefulness of unfolding preprocessing is illustrated for various time series. The experimental results show the meaningfulness of the clustering when applied on adequately unfolded time series.