title: Unfolding preprocessing for meaningful time series clustering.
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.
authors.affiliation: Machine Learning Group--DICE, Université Catholique de Louvain, Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium. simon@dice.ucl.ac.be
authors.forename: Geoffroy
authors.initials: G
authors.lastname: Simon
authors.forename: John A
authors.initials: JA
authors.lastname: Lee
authors.forename: Michel
authors.initials: M
authors.lastname: Verleysen
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors