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

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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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Dates and versions

hal-02361793 , version 1 (13-11-2019)

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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, ⟨10.1109/ICDMW.2019.00165⟩. ⟨hal-02361793⟩
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