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Projection-based curve clustering

Abstract : This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat à l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU-time consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centers found by the clustering method based on projections, compared to the “true” ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem.
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Contributor : Aurélie Fischer <>
Submitted on : Sunday, February 13, 2011 - 10:15:29 PM
Last modification on : Thursday, March 21, 2019 - 1:10:20 PM
Document(s) archivé(s) le : Saturday, May 14, 2011 - 4:23:20 AM


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  • HAL Id : hal-00565541, version 1


Benjamin Auder, Aurélie Fischer. Projection-based curve clustering. 2011. ⟨hal-00565541⟩



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