Cette thèse est à l'intersection de deux domaines de recherche en plein expansion, à savoir la fouille de données et la bioinformatique. Avec l'émergence des bases de graphes au cours des dernières années, de nombreux efforts ont été consacrés à la fouille des sous-graphes fréquents. Mais le nombre de sous-graphes fréquents découverts est exponentiel, cela est dû principalement à la nature combinatoire des graphes. Beaucoup de sous-graphes fréquents ne sont pas pertinents parce qu'ils sont redondants ou tout simplement inutiles pour l'utilisateur. En outre, leur nombre élevé peut nuire ou même rendre parfois irréalisable toute utilisation ultérieure. La redondance dans les sous-graphes fréquents est principalement due à la similarité structurelle et / ou sémantique, puisque la plupart des sous-graphes découverts diffèrent légèrement dans leur structures et peuvent exprimer des significations similaires ou même identiques. Dans cette thèse, nous proposons deux approches de sélection des sous-graphes représentatifs parmi les fréquents afin d'éliminer la redondance. Chacune des approches proposées s'intéresse à un type spécifique de redondance. La première approche s'adresse à la redondance sémantique où la similarité entre les sous-graphes est mesurée en fonction de la similarité entre les étiquettes de leurs noeuds, en utilisant les connaissances de domaine. La deuxième approche s'adresse à la redondance structurelle où les sous-graphes sont représentés par des descripteurs topologiques définis par l'utilisateur, et la similarité entre les sous-graphes est mesurée en fonction de la distance entre leurs descriptions topologiques respectives. Les principales données d'application de cette thèse sont les structures 3D des protéines. Ce choix repose sur des raisons biologiques et informatiques. D'un point de vue biologique, les protéines jouent un rôle crucial dans presque tous les processus biologiques. Ils sont responsables d'une variété de fonctions physiologiques. D'un point de vue informatique, nous nous sommes intéressés à la fouille de données complexes. Les protéines sont un exemple parfait de ces données car elles sont faites de structures complexes composées d'acides aminés interconnectés qui sont eux-mêmes composées d'atomes interconnectés. Des grandes quantités de structures protéiques sont actuellement disponibles dans les bases de données en ligne. Les structures 3D des protéines peuvent être transformées en graphes où les acides aminés représentent les noeuds du graphe et leurs connexions représentent les arêtes. Cela permet d'utiliser des techniques de fouille de graphes pour les étudier. L'importance biologique des protéines et leur complexité ont fait d'elles des données d'application appropriées pour cette thèse. / This thesis is in the intersection of two proliferating research fields, namely data mining and bioinformatics. With the emergence of graph data in the last few years, many efforts have been devoted to mining frequent subgraphs from graph databases. Yet, the number of discovered frequentsubgraphs is usually exponential, mainly because of the combinatorial nature of graphs. Many frequent subgraphs are irrelevant because they are redundant or just useless for the user. Besides, their high number may hinder and even makes further explorations unfeasible. Redundancy in frequent subgraphs is mainly caused by structural and/or semantic similarities, since most discovered subgraphs differ slightly in structure and may infer similar or even identical meanings. In this thesis, we propose two approaches for selecting representative subgraphs among frequent ones in order to remove redundancy. Each of the proposed approaches addresses a specific type of redundancy. The first approach focuses on semantic redundancy where similarity between subgraphs is measured based on the similarity between their nodes' labels, using prior domain knowledge. The second approach focuses on structural redundancy where subgraphs are represented by a set of user-defined topological descriptors, and similarity between subgraphs is measured based on the distance between their corresponding topological descriptions. The main application data of this thesis are protein 3D-structures. This choice is based on biological and computational reasons. From a biological perspective, proteins play crucial roles in almost every biological process. They are responsible of a variety of physiological functions. From a computational perspective, we are interested in mining complex data. Proteins are a perfect example of such data as they are made of complex structures composed of interconnected amino acids which themselves are composed of interconnected atoms. Large amounts of protein structures are currently available in online databases, in computer analyzable formats. Protein 3D-structures can be transformed into graphs where amino acids are the graph nodes and their connections are the graph edges. This enables using graph mining techniques to study them. The biological importance of proteins, their complexity, and their availability in computer analyzable formats made them a perfect application data for this thesis.

This thesis is in the intersection of two proliferating research fields, namely data mining and bioinformatics. With the emergence of graph data in the last few years, many efforts have been devoted to mining frequent subgraphs from graph databases. Yet, the number of discovered frequentsubgraphs is usually exponential, mainly because of the combinatorial nature of graphs. Many frequent subgraphs are irrelevant because they are redundant or just useless for the user. Besides, their high number may hinder and even makes further explorations unfeasible. Redundancy in frequent subgraphs is mainly caused by structural and/or semantic similarities, since most discovered subgraphs differ slightly in structure and may infer similar or even identical meanings. In this thesis, we propose two approaches for selecting representative subgraphs among frequent ones in order to remove redundancy. Each of the proposed approaches addresses a specific type of redundancy. The first approach focuses on semantic redundancy where similarity between subgraphs is measured based on the similarity between their nodes' labels, using prior domain knowledge. The second approach focuses on structural redundancy where subgraphs are represented by a set of user-defined topological descriptors, and similarity between subgraphs is measured based on the distance between their corresponding topological descriptions. The main application data of this thesis are protein 3D-structures. This choice is based on biological and computational reasons. From a biological perspective, proteins play crucial roles in almost every biological process. They are responsible of a variety of physiological functions. From a computational perspective, we are interested in mining complex data. Proteins are a perfect example of such data as they are made of complex structures composed of interconnected amino acids which themselves are composed of interconnected atoms. Large amounts of protein structures are currently available in online databases, in computer analyzable formats. Protein 3D-structures can be transformed into graphs where amino acids are the graph nodes and their connections are the graph edges. This enables using graph mining techniques to study them. The biological importance of proteins, their complexity, and their availability in computer analyzable formats made them a perfect application data for this thesis.