Advances in Knowledge Discovery and Management

ISBN-13: 9783642263712 / Angielski / Miękka / 2012 / 335 str.

Fabrice Guillet; Gilbert Ritschard; Djamel A. Zighed

Opis

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.

Spis treści

Supervised Learning and Information Retrieval.- Discrepancy Analysis of Complex Objects Using Dissimilarities.- A Bayes Evaluation Criterion for Decision Trees.- Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees.- Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval.- Toward a Better Integration of Spatial Relations in Learning with Graphical Models.- Unsupervised Learning.- Multigranular Manipulations for OLAP Querying.- A New Approach for Unsupervised Classification in Image Segmentation.- Cluster-Dependent Feature Selection through a Weighted Learning Paradigm.- Two Variants of the OKM for Overlapping Clustering.- A Stable Decomposition Algorithm for Dynamic Social Network Analysis.- Security and Data Streaming.- An Hybrid Data Stream Summarizing Approach by Sampling and Clustering.- SPAMS: A Novel Incremental Approach for Sequential Pattern Mining in Data Streams.- Mining Common Outliers for Intrusion Detection.- Intrusion Detections in Collaborative Organizations by Preserving Privacy.- Ontologies and Semantic.- Alignment-Based Partitioning of Large-Scale Ontologies.- Learning Ontologies with Deep Class Hierarchies by Mining the Content of Relational Databases.- Semantic Analysis for the Geospatial Semantic Web.- Statistically Valid Links and Anti-links BetweenWords and Between Documents: Applying TourneBool Randomization Test to a Reuters Collection.