During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for "Extraction et Gestion des Connaissances" in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for "Extraction et Gestion des Connaissances" in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

This volume contains 20 papers selected for presentation at the Third Inter- tional Workshopon Mining Complex Data-MCD2007-held in Warsaw, Poland, September 17-21, 2007. MCD is a workshop series that started in conjunction with the 5th IEEE International Conference on Data Mining (ICDM) in Ho- ton, Texas, November27-30,2005.ThesecondMCDworkshopwasheldagainin conjunction with the ICDM Conference in Hong Kong, December 18-22, 2006. Data mining and knowledge discovery, as stated in their early de?nition, can today be considered as stable ?elds with numerous e?cient methods and studies that have been proposed to extract knowledge from data. Nevertheless, the famous golden nugget is still challenging. Actually, the context evolved since the ?rst de?nition of the KDD process, and knowledge now has to be extracted from data becoming more and more complex. In the frameworkof data mining, many softwaresolutions were developedfor theextractionofknowledgefromtabulardata(whicharetypicallyobtainedfrom relationaldatabases).Methodologicalextensionswereproposedtodealwithdata initiallyobtainedfromothersources, e.g., inthecontextofnaturallanguage(text mining) and image (image mining). KDD has thus evolved following a unimodal scheme instantiated according to the type of the underlying data (tabular data, text, images, etc.), which, at the end, always leads to working on the classical double entry tabular format. However, in a large number of application domains, this unimodal approach appears to be too restrictive. Consider for instance a corpus of medical

This book constitutes the refereed proceedings of the 13th International Symposium on Methodologies for Intelligent Systems, ISMIS 2002, held in Lyon, France, in June 2002.The 63 revised full papers presented were carefully reviewed and selected from around 160 submissions. The book offers topical sections on learning and knowledge discovery, intelligent user interfaces and ontologies, logic for AI, knowledge representation and reasoning, intelligent information retrieval, soft computing, intelligent information systems, and methodologies.

This book constitutes the refereed proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, held in Lyon, France in September 2000. The 86 revised papers included in the book correspond to the 29 oral presentations and 57 posters presented at the conference. They were carefully reviewed and selected from 147 submissions. The book offers topical sections on new directions, rules and trees, databases and reward-based learning, classification, association rules and exceptions, instance-based discovery, clustering, and time series analysis.