A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp

Saeid Soheily-Khah 1, * Ahlame Douzal-Chouakria 1 Eric Gaussier 1
* Auteur correspondant
1 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Estimating the centroid of a set of time series under time warp is a major topic for many temporal data mining applications, as summarization a set of time series, prototype extraction or clustering. The task is challenging as the estimation of centroid of time series faces the problem of multiple temporal alignments. This work compares the major progressive and iterative centroid estimation methods, under the dynamic time warping, which currently is the most relevant similarity measure in this context.
Keywords : Centroid estimation Multiple temporal alignment Dynamic time warping Time series
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Chapitre d'ouvrage
Advanced Analysis and Learning on Temporal Data, 9785, Springer, pp.144-156, 2016, Lecture Notes in Computer Science, 978-3-319-44411-6. <10.1007/978-3-319-44412-3_10>. <https://link.springer.com/>
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Contributeur : Saeid Soheily-Khah <>
Soumis le : vendredi 19 mai 2017 - 13:20:31
Dernière modification le : dimanche 21 mai 2017 - 01:02:56

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Saeid Soheily-Khah, Ahlame Douzal-Chouakria, Eric Gaussier. A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp. Advanced Analysis and Learning on Temporal Data, 9785, Springer, pp.144-156, 2016, Lecture Notes in Computer Science, 978-3-319-44411-6. <10.1007/978-3-319-44412-3_10>. <https://link.springer.com/>. <hal-01385018>

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