Academic literature on the topic 'Fouille de données hybride'
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Journal articles on the topic "Fouille de données hybride":
Ganachaud, Clément, Ludovic Seifert, and David Adé. "L’importation de méthodes non-supervisées en fouille de données dans le programme de recherche empirique et technologique du cours d’action : Apports et réflexions critiques." Staps N° 141, no. 3 (January 17, 2024): 97–108. http://dx.doi.org/10.3917/sta.141.0097.
Hai, Phan Nhat, Pascal Poncelet, and Maguelone Teisseire. "Get_Move : fouille de données d’objets mobiles." Ingénierie des systèmes d'information 18, no. 4 (August 31, 2013): 145–69. http://dx.doi.org/10.3166/isi.18.4.145-169.
Mathieux, Néguine. "Myrina : nouvelles données d’une fouille du XIXe siècle." Dialogues d'histoire ancienne 37, no. 2 (2011): 183–91. http://dx.doi.org/10.3406/dha.2011.3277.
Ayrault, Clémence, and Sandy Goury. "Administrateur de données éditoriales, un métier hybride." I2D - Information, données & documents 52, no. 3 (2015): 11. http://dx.doi.org/10.3917/i2d.153.0011.
Schiappa, R., Y. Chateau, J. Gal, G. Daideri, P. Lemoine, E. Besrest, F. Paugam, E. François, J. Viotti, and E. Chamorey. "Fouille de données : comment valoriser les ressources de données médicales dans les centres hospitaliers ?" Revue d'Épidémiologie et de Santé Publique 66 (May 2018): S132—S133. http://dx.doi.org/10.1016/j.respe.2018.03.338.
Monmarché, Nicolas, Christiane Guinot, and Gilles Venturini. "Fouille visuelle et classification de données par nuage d'insectes volants." Revue d'intelligence artificielle 16, no. 6 (December 1, 2002): 729–52. http://dx.doi.org/10.3166/ria.16.729-752.
Novelli, Noël, and David Auber. "Calcul et fouille visuelle orientée-pixel de cubes de données." Revue d'intelligence artificielle 22, no. 3-4 (August 1, 2008): 329–52. http://dx.doi.org/10.3166/ria.22.329-352.
Derai, Sid Ali, and Abdelhamid Kaabeche. "Modélisation et dimensionnement d’un système hybride Eolien/ Photovoltaïque autonome." Journal of Renewable Energies 19, no. 2 (January 9, 2024): 265–76. http://dx.doi.org/10.54966/jreen.v19i2.566.
Hachour, Hakim. "De la fouille à la visualisation de données : un processus interprétatif." I2D - Information, données & documents 52, no. 2 (2015): 42. http://dx.doi.org/10.3917/i2d.152.0042.
Casali, Alain, Rosine Cicchetti, and Lotfi Lakhal. "Treillis cubes contraints pour la fouille de bases de données multidimensionnelles." Techniques et sciences informatiques 22, no. 10 (December 1, 2003): 1325–52. http://dx.doi.org/10.3166/tsi.22.1325-1352.
Dissertations / Theses on the topic "Fouille de données hybride":
Shahzad, Atif. "Une Approche Hybride de Simulation-Optimisation Basée sur la fouille de Données pour les problèmes d'ordonnancement." Phd thesis, Université de Nantes, 2011. http://tel.archives-ouvertes.fr/tel-00647353.
Shahzad, Muhammad Atif. "Une approche hybride de simulation-optimisation basée sur la fouille de données pour les problèmes d'ordonnancement." Nantes, 2011. http://archive.bu.univ-nantes.fr/pollux/show.action?id=53c8638a-977a-4b85-8c12-6dc88d92f372.
A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based upon seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a set of rules capable of approximating the efficient solutions for a job shop scheduling problem (JSSP). A data mining based scheduling framework is presented and implemented for a job shop problem with maximum lateness and mean tardiness as the scheduling objectives. The results obtained are very promising
Theobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.
In this thesis, we address the issue of trust in deep learning predictive systems in two complementary research directions. The first line of research focuses on the ability of AI to estimate its level of uncertainty in its decision-making as accurately as possible. The second line, on the other hand, focuses on the explainability of these systems, that is, their ability to convince human users of the soundness of their predictions.The problem of estimating the uncertainties is addressed from the perspective of Bayesian Deep Learning. Bayesian Neural Networks assume a probability distribution over their parameters, which allows them to estimate different types of uncertainties. First, aleatoric uncertainty which is related to the data, but also epistemic uncertainty which quantifies the lack of knowledge the model has on the data distribution. More specifically, this thesis proposes a Bayesian neural network can estimate these uncertainties in the context of a multivariate regression task. This model is applied to the regression of complex ellipticities on galaxy images as part of the ANR project "AstroDeep''. These images can be corrupted by different sources of perturbation and noise which can be reliably estimated by the different uncertainties. The exploitation of these uncertainties is then extended to galaxy mapping and then to "coaching'' the Bayesian neural network. This last technique consists of generating increasingly complex data during the model's training process to improve its performance.On the other hand, the problem of explainability is approached from the perspective of counterfactual explanations. These explanations consist of identifying what changes to the input parameters would have led to a different prediction. Our contribution in this field is based on the generation of counterfactual explanations relying on a variational autoencoder (VAE) and an ensemble of predictors trained on the latent space generated by the VAE. This method is particularly adapted to high-dimensional data, such as images. In this case, they are referred as counterfactual visual explanations. By exploiting both the latent space and the ensemble of classifiers, we can efficiently produce visual counterfactual explanations that reach a higher degree of realism than several state-of-the-art methods
Boudane, Abdelhamid. "Fouille de données par contraintes." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0403/document.
In this thesis, We adress the well-known clustering and association rules mining problems. Our first contribution introduces a new clustering framework, where complex objects are described by propositional formulas. First, we extend the two well-known k-means and hierarchical agglomerative clustering techniques to deal with these complex objects. Second, we introduce a new divisive algorithm for clustering objects represented explicitly by sets of models. Finally, we propose a propositional satisfiability based encoding of the problem of clustering propositional formulas without the need for an explicit representation of their models. In a second contribution, we propose a new propositional satisfiability based approach to mine association rules in a single step. The task is modeled as a propositional formula whose models correspond to the rules to be mined. To highlight the flexibility of our proposed framework, we also address other variants, namely the closed, minimal non-redundant, most general and indirect association rules mining tasks. Experiments on many datasets show that on the majority of the considered association rules mining tasks, our declarative approach achieves better performance than the state-of-the-art specialized techniques
Cohen, Jérémy E. "Fouille de données tensorielles environnementales." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT054/document.
Among commonly used data mining techniques, few are those which are able to take advantage of the multiway structure of data in the form of a multiway array. In contrast, tensor decomposition techniques specifically look intricate processes underlying the data, where each of these processes can be used to describe all ways of the data array. The work reported in the following pages aims at incorporating various external knowledge into the tensor canonical polyadic decomposition, which is usually understood as a blind model. The first two chapters of this manuscript introduce tensor decomposition techniques making use respectively of a mathematical and application framework. In the third chapter, the many faces of constrained decompositions are explored, including a unifying framework for constrained decomposition, some decomposition algorithms, compression and dictionary-based tensor decomposition. The fourth chapter discusses the inclusion of subject variability modeling when multiple arrays of data are available stemming from one or multiple subjects sharing similarities. State of the art techniques are studied and expressed as particular cases of a more general flexible coupling model later introduced. The chapter ends on a discussion on dimensionality reduction when subject variability is involved, as well a some open problems
Turmeaux, Teddy. "Contraintes et fouille de données." Orléans, 2004. http://www.theses.fr/2004ORLE2048.
Prudhomme, Elie. "Représentation et fouille de données volumineuses." Thesis, Lyon 2, 2009. http://www.theses.fr/2009LYO20048/document.
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Braud, Agnès. "Fouille de données par algorithmes génétiques." Orléans, 2002. http://www.theses.fr/2002ORLE2011.
Francisci, Dominique. "Techniques d'optimisation pour la fouille de données." Phd thesis, Université de Nice Sophia-Antipolis, 2004. http://tel.archives-ouvertes.fr/tel-00216131.
Collard, Martine. "Fouille de données, Contributions Méthodologiques et Applicatives." Habilitation à diriger des recherches, Université Nice Sophia Antipolis, 2003. http://tel.archives-ouvertes.fr/tel-01059407.
Books on the topic "Fouille de données hybride":
Tan, Pang-Ning. Introduction to Data Mining. ADDISON WESLEY PUBLI, 2006.
Web metrics for library and information professionals. Facet Publishing, 2013.
Stuart, David. Web Metrics for Library and Information Professionals. Facet Publishing, 2017.
Big Data Work Dispelling The Myths Uncovering The Opportunities. Harvard Business Press, 2014.
Book chapters on the topic "Fouille de données hybride":
Bayat, Sahar, Marc Cuggia, Delphine Rossille, and Luc Frimar. "Prédire l’accès à la liste d’attente de transplantation rénale: comparaison de deux méthodes de fouille de données." In Informatique et Santé, 239–50. Paris: Springer Paris, 2009. http://dx.doi.org/10.1007/978-2-287-99305-3_22.
Goetz, Christophe, Aurélien Zang, and Nicolas Jay. "Apports d’une méthode de fouille de données pour la détection des cancers du sein incidents dans les données du programme de médicalisation des systèmes d’information." In Informatique et Santé, 189–99. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0285-5_17.
"3. Les données de la fouille." In Une ferme seigneuriale au XIVe siècle. Éditions de la Maison des sciences de l’homme, 1989. http://dx.doi.org/10.4000/books.editionsmsh.34495.
"Walīlā aux Moyen Age : les données de la fouille." In Volubilis après Rome, 74–108. BRILL, 2018. http://dx.doi.org/10.1163/9789004371583_010.
PROVAN, Gregory. "Diagnostic des systèmes stochastiques." In Diagnostic et commande à tolérance de fautes 1, 145–66. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9058.ch4.
Nagatsuka, Makoto. "L’exception du droit d’auteur japonais favorisant la fouille de texte et de données (TDM)." In L'entreprise et l'intelligence artificielle - Les réponses du droit, 315–32. Presses de l’Université Toulouse 1 Capitole, 2022. http://dx.doi.org/10.4000/books.putc.15424.
Lechevrel, Nadège. "Chapitre 5. Fouille de données textuelles et recherche documentaire automatiques pour l’histoire des théories linguistiques." In Apparenter la pensée ?, 219. Editions Matériologiques, 2014. http://dx.doi.org/10.3917/edmat.charb.2014.01.0219.
Gérard Yao, Kouamé. "Les seuils du théâtre." In D'un seuil à l'autre, 189–97. Editions des archives contemporaines, 2017. http://dx.doi.org/10.17184/eac.771.
Conference papers on the topic "Fouille de données hybride":
Sureau, Florian, Fatma Bouali, and Gilles Venturini. "Extension de DataTube pour la fouille visuelle de données temporelles." In the 20th International Conference of the Association Francophone d'Interaction Homme-Machine. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1512714.1512722.
Reports on the topic "Fouille de données hybride":
Nédellec, Claire, Adeline Nazarenko, Francis André, Catherine Balivo, Béatrice Daille, Anastasia Drouot, Jorge Flores, et al. Recommandations sur l’analyse automatique de documents : acquisition, gestion, exploration. Ministère de l'enseignement supérieur et de la recherche, September 2019. http://dx.doi.org/10.52949/10.
Béjaoui, Ali, Sylvie St-Onge, Ingrid Peignier, and Felix Bellesteros Leivas. Les diverses facettes du travail hybride. Seconds résultats d’un projet longitudinal de recherche. CIRANO, October 2023. http://dx.doi.org/10.54932/qymj5601.
Rousseau, Henri-Paul. Gutenberg, L’université et le défi numérique. CIRANO, December 2022. http://dx.doi.org/10.54932/wodt6646.