Literatura científica selecionada sobre o tema "Fouille de règles d'associations"
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Artigos de revistas sobre o assunto "Fouille de règles d'associations"
Di Jorio, Lisa, Sandra Bringay, Denis Brouillet, Anne Laurent, Sophie Martin e Maguelonne Teisseire. "Fouille de données issues d'études psychologiques liées au vieillissement. Extraction de règles graduelles". Techniques et sciences informatiques 29, n.º 8-9 (20 de novembro de 2010): 939–57. http://dx.doi.org/10.3166/tsi.29.939-957.
Texto completo da fonteAernout, E., G. Ficheur, M. Djennaoui, E. Chazard e R. Beuscart. "Codage automatisé à partir des comptes-rendus d’actes : construction et évaluation de règles de prédiction par une méthode mixte associant fouille de texte et validation experte". Revue d'Épidémiologie et de Santé Publique 62 (março de 2014): S93. http://dx.doi.org/10.1016/j.respe.2014.01.070.
Texto completo da fonteAparicio-Valdez, Luis. "La gestion empresarial en latinoamérica y su impacto en las relaciones laborales". Articles 44, n.º 1 (12 de abril de 2005): 124–48. http://dx.doi.org/10.7202/050476ar.
Texto completo da fonteTeses / dissertações sobre o assunto "Fouille de règles d'associations"
Idoudi, Rihab. "Fouille de connaissances en diagnostic mammographique par ontologie et règles d'association". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0005/document.
Texto completo da fonteFacing the significant complexity of the mammography area and the massive changes in its data, the need to contextualize knowledge in a formal and comprehensive modeling is becoming increasingly urgent for experts. It is within this framework that our thesis work focuses on unifying different sources of knowledge related to the domain within a target ontological modeling. On the one hand, there is, nowadays, several mammographic ontological modeling, where each resource has a distinct perspective area of interest. On the other hand, the implementation of mammography acquisition systems makes available a large volume of information providing a decisive competitive knowledge. However, these fragments of knowledge are not interoperable and they require knowledge management methodologies for being comprehensive. In this context, we are interested on the enrichment of an existing domain ontology through the extraction and the management of new knowledge (concepts and relations) derived from two scientific currents: ontological resources and databases holding with past experiences. Our approach integrates two knowledge mining levels: The first module is the conceptual target mammographic ontology enrichment with new concepts extracting from source ontologies. This step includes three main stages: First, the stage of pre-alignment. The latter consists on building for each input ontology a hierarchy of fuzzy conceptual clusters. The goal is to reduce the alignment task from two full ontologies to two reduced conceptual clusters. The second stage consists on aligning the two hierarchical structures of both source and target ontologies. Thirdly, the validated alignments are used to enrich the reference ontology with new concepts in order to increase the granularity of the knowledge base. The second level of management is interested in the target mammographic ontology relational enrichment by novel relations deducted from domain database. The latter includes medical records of mammograms collected from radiology services. This section includes four main steps: i) the preprocessing of textual data ii) the application of techniques for data mining (or knowledge extraction) to extract new associations from past experience in the form of rules, iii) the post-processing of the generated rules. The latter is to filter and classify the rules in order to facilitate their interpretation and validation by expert, vi) The enrichment of the ontology by new associations between concepts. This approach has been implemented and validated on real mammographic ontologies and patient data provided by Taher Sfar and Ben Arous hospitals. The research work presented in this manuscript relates to knowledge using and merging from heterogeneous sources in order to improve the knowledge management process
Boudane, Abdelhamid. "Fouille de données par contraintes". Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0403/document.
Texto completo da fonteIn 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
Bouker, Slim. "Contribution à l'extraction des règles d'association basée sur des préférences". Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22585/document.
Texto completo da fonteCouturier, Olivier. "Contribution à la fouille de données : règles d'association et interactivité au sein d'un processus d'extraction de connaissances dans les données". Artois, 2005. http://www.theses.fr/2005ARTO0410.
Texto completo da fonteBothorel, Gwenael. "Algorithmes automatiques pour la fouille visuelle de données et la visualisation de règles d’association : application aux données aéronautiques". Phd thesis, Toulouse, INPT, 2014. http://oatao.univ-toulouse.fr/13783/1/bothorel.pdf.
Texto completo da fonteSzathmary, Laszlo. "Méthodes symboliques de fouille de données avec la plate-forme Coron". Phd thesis, Université Henri Poincaré - Nancy I, 2006. http://tel.archives-ouvertes.fr/tel-00336374.
Texto completo da fonteLes contributions principales de cette thèse sont : (1) nous avons développé et adapté des algorithmes pour trouver les règles d'association minimales non-redondantes ; (2) nous avons défini une nouvelle base pour les règles d'associations appelée “règles fermées” ; (3) nous avons étudié un champ de l'ECBD important mais relativement peu étudié, à savoir l'extraction des motifs rares et des règles d'association rares ; (4) nous avons regroupé nos algorithmes et une collection d'autres algorithmes ainsi que d'autres opérations auxiliaires d'ECBD dans une boîte à outils logicielle appelée Coron.
Azé, Jérôme. "Extraction de Connaissances à partir de Données Numériques et Textuelles". Phd thesis, Université Paris Sud - Paris XI, 2003. http://tel.archives-ouvertes.fr/tel-00011196.
Texto completo da fonteL'analyse de telles données est souvent contrainte par la définition d'un support minimal utilisé pour filtrer les connaissances non intéressantes.
Les experts des données ont souvent des difficultés pour déterminer ce support.
Nous avons proposé une méthode permettant de ne pas fixer un support minimal et fondée sur l'utilisation de mesures de qualité.
Nous nous sommes focalisés sur l'extraction de connaissances de la forme "règles d'association".
Ces règles doivent vérifier un ou plusieurs critères de qualité pour être considérées comme intéressantes et proposées à l'expert.
Nous avons proposé deux mesures de qualité combinant différents critères et permettant d'extraire des règles intéressantes.
Nous avons ainsi pu proposer un algorithme permettant d'extraire ces règles sans utiliser la contrainte du support minimal.
Le comportement de notre algorithme a été étudié en présence de données bruitées et nous avons pu mettre en évidence la difficulté d'extraire automatiquement des connaissances fiables à partir de données bruitées.
Une des solutions que nous avons proposée consiste à évaluer la résistance au bruit de chaque règle et d'en informer l'expert lors de l'analyse et de la validation des connaissances obtenues.
Enfin, une étude sur des données réelles a été effectuée dans le cadre d'un processus de fouille de textes.
Les connaissances recherchées dans ces textes sont des règles d'association entre des concepts définis par l'expert et propres au domaine étudié.
Nous avons proposé un outil permettant d'extraire les connaissances et d'assister l'expert lors de la validation de celles-ci.
Les différents résultats obtenus montrent qu'il est possible d'obtenir des connaissances intéressantes à partir de données textuelles en minimisant la sollicitation de l'expert dans la phase d'extraction des règles d'association.
Fu, Huaiguo. "Algorithmique des treillis de concepts : application à la fouille de données". Artois, 2005. http://www.theses.fr/2005ARTO0401.
Texto completo da fonteOur main concern in this thesis is concept (or galois) lattices and its application to data mining. We achieve a comparison of different concept lattices algorithms on benchmarks taken from UCI. During this comparison, we analyse the duality phenomenon between objects and attributes on each algorithm performance. This analysis allows to show that the running time of an algorithm may considerably vary when using the formal context or the transposed context. Using the Divide-and-Conquer paradigm, we design a new concept lattice algorithm, ScalingNextClosure, which decomposes the search space in many partitions and builds formal concepts for each partition independently. By reducing the search space, ScalingNextClosure can deal efficiently with few memory space and thus treat huge formal context, but only if the whole context can be loaded in the memory. An experimental comparison between NextClosure and ScalingNextClosure shows the efficiency of such decomposition approach. In any huge dataset, ScalingNextClosure runs faster than NextClosure on a sequential machine, with an average win factor equal to 10. Another advantage of ScalingNextClosure is that it can be easily implemented on a distributed or parallel architecture. Mining frequent closed itemsets (FCI) is a subproblem of mining association rules. We adapt ScalingNextClosure to mine frequent closed itemsets, and design a new algorithm, called PFC. PFC uses the support measure to prune the search space within one partition. An experimental comparison conducted on a sequential architecture, between PFC with one of the efficient FCI system, is discussed
Papon, Pierre-Antoine. "Extraction optimisée de règles d'association positives et négatives intéressantes". Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22702/document.
Texto completo da fonteThe purpose of data mining is to extract knowledge from large amount of data. The extracted knowledge can take different forms. In this work, we will seek to extract knowledge only in the form of positive association rules and negative association rules. A negative association rule is a rule in which the presence and the absence of a variable can be used. When considering the absence of variables in the study, we will expand the semantics of knowledge and extract undetectable information by the positive association rules mining methods. This will, for example allow doctors to find characteristics that prevent disease instead of searching characteristics that cause a disease. Nevertheless, adding the negation will cause various challenges. Indeed, as the absence of a variable is usually more important than the presence of these same variables, the computational costs will increase exponentially and the risk to extract a prohibitive number of rules, which are mostly redundant and uninteresting, will also increase. In order to address these problems, our proposal, based on the famous Apriori algorithm, does not rely on frequent itemsets as other methods do. We define a new type of itemsets : the reasonably frequent itemsets which will improve the quality of the rules. We also rely on the M G measure to know which forms of rules should be mined but also to remove uninteresting rules. We also use meta-rules to allow us to infer the interest of a negative rule from a positive one. Moreover, our algorithm will extract a new type of negative rules that seems interesting : the rules for which the antecedent and the consequent are conjunctions of negative itemsets. Our study ends with a quantitative and qualitative comparison with other positive and negative association rules mining algorithms on various databases of the literature. Our software ARA (Association Rules Analyzer ) facilitates the qualitative analysis of the algorithms by allowing to compare intuitively the algorithms and to apply in post-process treatments various quality measures. Finally, our proposal improves the extraction in the number and the quality of the extracted rules but also in the rules search path
Mondal, Kartick Chandra. "Algorithmes pour la fouille de données et la bio-informatique". Thesis, Nice, 2013. http://www.theses.fr/2013NICE4049.
Texto completo da fonteKnowledge pattern extraction is one of the major topics in the data mining and background knowledge integration domains. Out of several data mining techniques, association rule mining and bi-clustering are two major complementary tasks for these topics. These tasks gained much importance in many domains in recent years. However, no approach was proposed to perform them in one process. This poses the problems of resources required (memory, execution times and data accesses) to perform independent extractions and of the unification of the different results. We propose an original approach for extracting different categories of knowledge patterns while using minimum resources. This approach is based on the frequent closed patterns theoretical framework and uses a novel suffix-tree based data structure to extract conceptual minimal representations of association rules, bi-clusters and classification rules. These patterns extend the classical frameworks of association and classification rules, and bi-clusters as data objects supporting each pattern and hierarchical relationships between patterns are also extracted. This approach was applied to the analysis of HIV-1 and human protein-protein interaction data. Analyzing such inter-species protein interactions is a recent major challenge in computational biology. Databases integrating heterogeneous interaction information and biological background knowledge on proteins have been constructed. Experimental results show that the proposed approach can efficiently process these databases and that extracted conceptual patterns can help the understanding and analysis of the nature of relationships between interacting proteins