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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Generalizing association rules in n-ary relations : application to dynamic graph analysis

Nguyen, Thi Kim Ngan 23 October 2012 (has links) (PDF)
Pattern discovery in large binary relations has been extensively studied. An emblematic success in this area concerns frequent itemset mining and its post-processing that derives association rules. In this case, we mine binary relations that encode whether some properties are satisfied or not by some objects. It is however clear that many datasets correspond to n-ary relations where n > 2. For example, adding spatial and/or temporal dimensions (location and/or time when the properties are satisfied by the objects) leads to the 4-ary relation Objects x Properties x Places x Times. Therefore, we study the generalization of association rule mining within arbitrary n-ary relations: the datasets are now Boolean tensors and not only Boolean matrices. Unlike standard rules that involve subsets of only one domain of the relation, in our setting, the head and the body of a rule can include arbitrary subsets of some selected domains. A significant contribution of this thesis concerns the design of interestingness measures for such generalized rules: besides a frequency measures, two different views on rule confidence are considered. The concept of non-redundant rules and the efficient extraction of the non-redundant rules satisfying the minimal frequency and minimal confidence constraints are also studied. To increase the subjective interestingness of rules, we then introduce disjunctions in their heads. It requires to redefine the interestingness measures again and to revisit the redundancy issues. Finally, we apply our new rule discovery techniques to dynamic relational graph analysis. Such graphs can be encoded into n-ary relations (n ≥ 3). Our use case concerns bicycle renting in the Vélo'v system (self-service bicycle renting in Lyon). It illustrates the added-value of some rules that can be computed thanks to our software prototypes.
2

Generalizing association rules in n-ary relations : application to dynamic graph analysis / Généralisation des règles d'association dans des relations n-aires : application à l'analyse de graphes dynamiques

Nguyen, Thi Kim Ngan 23 October 2012 (has links)
Le calcul de motifs dans de grandes relations binaires a été très étudié. Un succès emblématique concerne la découverte d'ensembles fréquents et leurs post-traitements pour en dériver des règles d'association. Il s'agit de calculer des motifs dans des relations binaires qui enregistrent quelles sont les propriétés satisfaites par des objets. En fait, de nombreux jeux de données se présentent naturellement comme des relations n-aires (avec n > 2). Par exemple, avec l'ajout de dimensions spatiales et/ou temporelles (lieux et/ou temps où les propriétés sont enregistrées), la relation binaire Objets x Propriétés est étendue à une relation 4-aire Objets x Propriétés x Lieux x Temps. Nous avons généralisé le concept de règle d'association dans un tel contexte multi-dimensionnel. Contrairement aux règles usuelles qui n'impliquent que des sous-ensembles d'un seul domaine de la relation, les prémisses et les conclusions de nos règles peuvent impliquer des sous-ensembles arbitraires de certains domaines. Nous avons conçu des mesures de fréquence et de confiance pour définir la sémantique de telles règles et c'est une contribution significative de cette thèse. Le calcul exhaustif de toutes les règles qui ont des fréquences et confiances suffisantes et l'élimination des règles redondantes ont été étudiés. Nous proposons ensuite d'introduire des disjonctions dans les conclusions des règles, ce qui nécessite de retravailler les définitions des mesures d'intérêt et les questions de redondance. Pour ouvrir un champ d'application original, nous considérons la découverte de règles dans des graphes relationnels dynamiques qui peuvent être codés dans des relations n-aires (n ≥ 3). Une application à l'analyse des usages de bicyclettes dans le système Vélo'v (système de Vélos en libre-service du Grand Lyon) montre quelques usages possibles des règles que nous savons calculer avec nos prototypes logiciels. / Pattern discovery in large binary relations has been extensively studied. An emblematic success in this area concerns frequent itemset mining and its post-processing that derives association rules. In this case, we mine binary relations that encode whether some properties are satisfied or not by some objects. It is however clear that many datasets correspond to n-ary relations where n > 2. For example, adding spatial and/or temporal dimensions (location and/or time when the properties are satisfied by the objects) leads to the 4-ary relation Objects x Properties x Places x Times. Therefore, we study the generalization of association rule mining within arbitrary n-ary relations: the datasets are now Boolean tensors and not only Boolean matrices. Unlike standard rules that involve subsets of only one domain of the relation, in our setting, the head and the body of a rule can include arbitrary subsets of some selected domains. A significant contribution of this thesis concerns the design of interestingness measures for such generalized rules: besides a frequency measures, two different views on rule confidence are considered. The concept of non-redundant rules and the efficient extraction of the non-redundant rules satisfying the minimal frequency and minimal confidence constraints are also studied. To increase the subjective interestingness of rules, we then introduce disjunctions in their heads. It requires to redefine the interestingness measures again and to revisit the redundancy issues. Finally, we apply our new rule discovery techniques to dynamic relational graph analysis. Such graphs can be encoded into n-ary relations (n ≥ 3). Our use case concerns bicycle renting in the Vélo'v system (self-service bicycle renting in Lyon). It illustrates the added-value of some rules that can be computed thanks to our software prototypes.

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