Horn axiomatizations for sequential data

AbstractWe propose a notion of deterministic association rules for ordered data. We prove that our proposed rules can be formally justified by a purely logical characterization, namely, a natural notion of empirical Horn approximation for ordered data which involves background Horn conditions; these ensure the consistency of the propositional theory obtained with the ordered context. The whole framework resorts to concept lattice models from Formal Concept Analysis, but adapted to ordered contexts. We also discuss a general method to mine these rules that can be easily incorporated into any algorithm for mining closed sequences, of which there are already some in the literature

Mining frequent closed rooted trees

Many knowledge representation mechanisms are based on tree-like structures, thus symbolizing the fact that certain pieces of information are related in one sense or another. There exists a well-studied process of closure-based data mining in the itemset framework: we consider the extension of this process into trees. We focus mostly on the case where labels on the nodes are nonexistent or unreliable, and discuss algorithms for closurebased mining that only rely on the root of the tree and the link structure. We provide a notion of intersection that leads to a deeper understanding of the notion of support-based closure, in terms of an actual closure operator. We describe combinatorial characterizations and some properties of ordered trees, discuss their applicability to unordered trees, and rely on them to design efficient algorithms for mining frequent closed subtrees both in the ordered and the unordered settings. Empirical validations and comparisons with alternative algorithms are provided.Postprint (author’s final draft

Etude, représentation et applications des traverses minimales d'un hypergraphe

This work is part of the field of the hypergraph theory and focuses on hypergraph minimal transversal. The problem of extracting the minimal transversals from a hypergraph received the interest of many researchers as shown the number of algorithms proposed in the literature, and this is mainly due to the solutions offered by the minimal transversal in various application areas such as databases, artificial intelligence, e-commerce, semantic web, etc. In view of the wide range of fields of minimal transversal application and the interest they generate, the objective of this thesis is to explore new application paths of minimal transversal by proposing methods to optimize the extraction. This has led to three proposed contributions in this thesis. The first approach takes advantage of the emergence of Web 2.0 and, therefore, social networks using minimal transversal for the detection of important actors within these networks. The second part of research in this thesis has focused on reducing the number of hypergraph minimal transversal. A concise and accurate representation of minimal transversal was proposed and is based on the construction of an irredundant hypergraph, hence are calculated the irredundant minimal transversal of the initial hypergraph. An application of this representation to the dependency inference problem is presented to illustrate the usefulness of this approach. The last approach includes the hypergraph decomposition into partial hypergraph the “local” minimal transversal are calculated and their Cartesian product can generate all the hypergraph transversal sets. Different experimental studies have shown the value of these proposed approachesCette thèse s'inscrit dans le domaine de la théorie des hypergraphes et s'intéresse aux traverses minimales des hypergraphes. L'intérêt pour l'extraction des traverses minimales est en nette croissance, depuis plusieurs années, et ceci est principalement dû aux solutions qu'offrent les traverses minimales dans divers domaines d'application comme les bases de données, l'intelligence artificielle, l'e-commerce, le web sémantique, etc. Compte tenu donc du large éventail des domaines d'application des traverses minimales et de l'intérêt qu'elles suscitent, l'objectif de cette thèse est donc d'explorer de nouvelles pistes d'application des traverses minimales tout en proposant des méthodes pour optimiser leur extraction. Ceci a donné lieu à trois contributions proposées dans cette thèse. La première approche tend à tirer profit de l'émergence du Web 2.0 et, par conséquent, des réseaux sociaux en utilisant les traverses minimales pour la détection des acteurs importants au sein de ces réseaux. La deuxième partie de recherche au cours de cette thèse s'est intéressé à la réduction du nombre de traverses minimales d'un hypergraphe. Ce nombre étant très élevé, une représentation concise et exacte des traverses minimales a été proposée et est basée sur la construction d'un hypergraphe irrédondant, d'où sont calculées les traverses minimales irrédondantes de l'hypergraphe initial. Une application de cette représentation au problème de l'inférence des dépendances fonctionnelles a été présentée pour illustrer l’intérêt de cette approche. La dernière approche s'est intéressée à la décomposition des hypergraphes en des hypergraphes partiels. Les traverses minimales de ces derniers sont calculées et leur produit cartésien permet de générer l'ensemble des traverses de l'hypergraphe. Les différentes études expérimentales menées ont montré l’intérêt de ces approches proposée

Formal Methods for Mining Structured Objects

X, 109 p. 51 illus.online resource