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

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.
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Advanced Analysis and Learning on Temporal Data, 9785, pp.144-156, 2016, Lecture Notes in Computer Science, 978-3-319-44411-6. <10.1007/978-3-319-44412-3_10>. <Springer International Publishing>
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https://hal.archives-ouvertes.fr/hal-01385018
Contributeur : Saeid Soheily-Khah <>
Soumis le : jeudi 20 octobre 2016 - 16:18:28
Dernière modification le : samedi 22 octobre 2016 - 01:02:44

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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, pp.144-156, 2016, Lecture Notes in Computer Science, 978-3-319-44411-6. <10.1007/978-3-319-44412-3_10>. <Springer International Publishing>. <hal-01385018>

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