A pattern-based mining system for exploring Displacement Field Time Series

Tuan Nguyen 1 Nicolas Méger 1 Christophe Rigotti 2, 3, 4 Catherine Pothier 5 Noel Gourmelen 6 Emmanuel Trouvé 1
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
5 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper presents the first available system for mining patterns from Displacement Field Time Series (DFTS) along with the confidence measures inherent to these series. It consists of four main modules for data preprocessing, pattern extraction, pattern ranking and pattern visualization. It is based on an efficient extraction of reliable grouped frequent sequential patterns and on swap randomization. It can be for example used to assess climate change impacts on glacier dynamics.
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Tuan Nguyen, Nicolas Méger, Christophe Rigotti, Catherine Pothier, Noel Gourmelen, et al.. A pattern-based mining system for exploring Displacement Field Time Series. 19th IEEE International Conference on Data Mining (ICDM) Demo, Nov 2019, Beijing, China. pp.1110-1113. ⟨hal-02361793⟩

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