Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Ahlame Douzal-Chouakria"'
Publikováno v:
Axioms, Vol 12, Iss 6, p 570 (2023)
Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a
Externí odkaz:
https://doaj.org/article/8e59458bcf2144e0ae90cc277448a917
Publikováno v:
Axioms; Volume 12; Issue 6; Pages: 570
Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a
Publikováno v:
Knowledge and Information Systems (KAIS)
Knowledge and Information Systems (KAIS), Springer, 2018, ⟨10.1007/s10115-018-1184-z⟩
Knowledge and Information Systems (KAIS), Springer, 2018, ⟨10.1007/s10115-018-1184-z⟩
Accuracy of the k-nearest neighbour ( $$k\hbox {NN}$$ ) classifier depends strongly on the ability of the used distance to induce k-nearest neighbours of the same class while keeping distant samples of different classes. For time series classificatio
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109240
ECML/PKDD (1)
ECML/PKDD, 2018
ECML/PKDD, 2018, Sep 2018, Dublin, Ireland
ECML/PKDD (1)
ECML/PKDD, 2018
ECML/PKDD, 2018, Sep 2018, Dublin, Ireland
This work proposes a time warp invariant sparse coding and dictionary learning framework for time series clustering, where both input samples and atoms define time series of different lengths that involve variable delays. For that, first an \(l_0\) s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f41d93b9857f91fa2671be11ba00e31
https://doi.org/10.1007/978-3-030-10925-7_22
https://doi.org/10.1007/978-3-030-10925-7_22
Autor:
Anh-Duong Nguyen, Ky T. Nguyen, Jean-Louis Pépin, Son T. Mai, Sébastien Bailly, Ahlame Douzal Chouakria, Sihem Amer-Yahia
Publikováno v:
Data Science and Engineering
Data Science and Engineering, Springer, 2018, 3 (4), pp.359-378
Data Science and Engineering, Vol 3, Iss 4, Pp 359-378 (2018)
Mai, T S, Amer-Yahia, S, Bailly, S, Pepin, J L, Chouakria, A D, Nguyen, K T & Nguyen, A-D 2018, ' Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis ', Data Science and Engineering, vol. 3, no. 4, pp. 359--378 . https://doi.org/10.1007/s41019-018-0080-6
Data Science and Engineering, Springer, 2018, 3 (4), pp.359-378
Data Science and Engineering, Vol 3, Iss 4, Pp 359-378 (2018)
Mai, T S, Amer-Yahia, S, Bailly, S, Pepin, J L, Chouakria, A D, Nguyen, K T & Nguyen, A-D 2018, ' Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis ', Data Science and Engineering, vol. 3, no. 4, pp. 359--378 . https://doi.org/10.1007/s41019-018-0080-6
We introduce a novel interactive framework to handle both instancelevel and temporal smoothness constraints for clustering large longitudinaldata and for tracking the cluster evolutions over time. It consists of a constrained clustering algorithm, ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cc8e3d54820cb2236bd4da20d833a05
https://hal.archives-ouvertes.fr/hal-02000491
https://hal.archives-ouvertes.fr/hal-02000491
Publikováno v:
Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2016, 75, ⟨10.1016/j.patrec.2016.03.007⟩
Pattern Recognition Letters, Elsevier, 2016, 75, ⟨10.1016/j.patrec.2016.03.007⟩
Generalize k-means-based clustering to temporal data under time warp.Extend time warp measures and temporal kernels to capture local temporal differences.Propose a tractable estimation of the cluster representatives under extended measures.Propose fa
Autor:
Thi Phuong Thao Tran, Saeed Varasteh Yazdi, Paul Honeine, Patrick Gallinari, Ahlame Douzal-Chouakria
Publikováno v:
Artificial Intelligence
Artificial Intelligence, Elsevier, 2020, 286, pp.103342. ⟨10.1016/j.artint.2020.103342⟩
Artificial Intelligence, Elsevier, 2020, 286, pp.103342. ⟨10.1016/j.artint.2020.103342⟩
International audience; Kernel methods are known to be effective to analyse complex objects by implicitly embedding them into some feature space. To interpret and analyse the obtained results, it is often required to restore in the input space the re
Publikováno v:
Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. 〈10.1016/j.patrec.2018.05.017〉
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. ⟨10.1016/j.patrec.2018.05.017⟩
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. 〈10.1016/j.patrec.2018.05.017〉
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. ⟨10.1016/j.patrec.2018.05.017⟩
Learning dictionary for sparse representing time series is an important issue to extract latent temporal features, reveal salient primitives and sparsely represent complex temporal data. Time series are challenging data, they are often of different d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61dbb3ba3b8e79ea431c2d5fc6bafebd
https://hal.archives-ouvertes.fr/hal-01811938
https://hal.archives-ouvertes.fr/hal-01811938
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783319914510
DASFAA (1)
DASFAA (1)
In this paper, we introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm, called CVQE+, which optimizes the clust
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3934705cab7146523c93a6a08d8e3192
https://doi.org/10.1007/978-3-319-91452-7_37
https://doi.org/10.1007/978-3-319-91452-7_37
Publikováno v:
Energy and Buildings
Energy and Buildings, Elsevier, 2015, 96 (109–117)
HAL
Energy and Buildings, Elsevier, 2015, 96 (109–117)
HAL
International audience; Non-Intrusive Load Monitoring (Nilm) deals with the disaggregation of in- dividual appliances from the total load at the smart meter level. This work proposes a generic methodology using temporal sequence classification algo-