Advances in Knowledge Discovery and Management : Volume 5 / / edited by Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed

• Record Nr.: UNINA9910254189303321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• ISBN: 3-319-23751-9
• Edizione: [1st ed. 2016.]
• Descrizione fisica: 1 online resource (XVIII, 137 p. 40 illus., 29 illus. in color.)
• Collana: Studies in Computational Intelligence, , 1860-949X ; ; 615
• Disciplina: 006.3
• Soggetti: Computational intelligence; Artificial intelligence; Operations research; Decision making; Computational Intelligence; Artificial Intelligence; Operations Research/Decision Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di contenuto: Intro -- Preface -- Structure of the Book -- Acknowledgments -- Review Committee -- Associated Reviewers -- Contents -- Editors and Contributors -- Part IApplications of KDMto Real Datasets -- A Study of the Spatio-Temporal Correlations in Mobile Calls Networks -- 1 Introduction -- 2 Antenna Clustering Based on Mobile Calls -- 2.1 Related Works -- 2.2 Applying the MODL Approach -- 3 Analysis of the Spatial Correlations -- 3.1 A Countrywide Analysis -- 3.2 A Local Analysis -- 4 Spatio-Temporal Analysis -- 5 Conclusion -- References -- Co-Clustering Network-Constrained Trajectory Data -- 1 Introduction -- 2 Clustering Approaches -- 2.1 Clustering the Projected Trajectory and Segment Graphs -- 2.2 Direct Co-Clustering of the Bipartite Graph -- 3 Experimental Study -- 3.1 Experimental Setting -- 3.2 Analysis of Trajectory Clusters -- 3.3 Mutual Analysis of Trajectory and Segment Clusters -- 4 Related Work -- 5 Conclusions -- References -- Medical Discourse and Subjectivity -- 1 Introduction ; -- 2 Building and Preparation of Material -- 2.1 Corpora -- 2.2 Resources -- 3 Method -- 3.1 Linguistic and Semantic Annotation of Corpora -- 3.2 Automatic Categorization -- 4 Presentation and Discussion of Results -- 4.1 Annotation and Its Evaluation -- 4.2 Automatic Categorization -- 5 Conclusion and Future Work -- References -- Part IIFoundations of KDM -- Relational Concept Analysis for Relational Data Exploration -- 1 Introduction -- 2 Formal Concept Analysis -- 3 RCA and Its Extension for Exploratory Analysis -- 3.1 Relational Concept Analysis -- 3.2 Exploratory Relational Concept Analysis -- 4 Exploration Examples -- 4.1 Exploring Links Between Traits and Physico-Chemical Parameters -- 4.2 Focusing on Specific Relations -- 5 Related Work -- 6 Discussion and Conclusion -- References.; Dynamic Recommender System: Using Cluster-Based Biases to Improve the Accuracy of the Predictions -- 1 Introduction -- 2 Preliminaries -- 2.1 Prediction Issue -- 2.2 Matrix Factorization -- 2.3 Biased MF -- 2.4 Dynamicity and Performance Requirements -- 3 Dynamic Recommendations -- 3.1 Why Clustering? -- 3.2 The CBMF Model -- 3.3 Integration of Incoming Ratings -- 3.4 Complexity Analysis -- 4 Experimental Evaluation -- 4.1 Implementation and Experimental Setup -- 4.2 Datasets -- 4.3 Initial Quality -- 4.4 Large Training Sets Improve Quality -- 4.5 Quantifying the Need for Online Integration -- 4.6 Robustness to Time of Our Online Integration Model -- 4.7 Quality Versus Performance Tradeoff for Online Integration -- 4.8 Benefit of Refactorization -- 5 Related Work -- 6 Conclusion -- References -- Mining (Soft-) Skypatterns Using Constraint Programming -- 1 Introduction -- 2 The Skypattern Mining Problem -- 2.1 Context and Definitions -- 2.2 Skypatterns -- 3 The Soft Skypattern Mining Problem -- 3.1 Edge-Skypatterns -- 3.2 δ-Skypatterns -- 4 Mining (Soft-) Skypatterns Using CP -- 4.1 CSP -- 4.2 Dynamic CSP -- 4.3 Mining Skypatterns Using Dynamic CSP -- 4.4 Example -- 4.5 Mining Soft Skypatterns Using Dynamic CSP -- 4.6 Pattern Encoding -- 4.7 Closedness Constraints -- 5 Related Work -- 6 Experimental Study -- 6.1 Experiments on UCI Benchmarks -- 6.2 Case Study: Discovering Toxicophores -- 7 Conclusion -- References -- Author Index.
• Sommario/riassunto: This book is a collection of representative and novel works done in Data Mining, Knowledge Discovery, Clustering and Classification that were originally presented in French at the EGC'2013 (Toulouse, France, January 2013) and EGC'2014 Conferences (Rennes, France, January 2014). These conferences were respectively the 13th and 14th editions of this event, which takes place each year and which is now successful and well-known in the French-speaking community. This community was structured in 2003 by the foundation of the French-speaking EGC society (EGC in French stands for "Extraction et Gestion des Connaissances" and means "Knowledge Discovery and Management", or KDM). This book is aiming at all researchers interested in these fields, including PhD or MSc students, and researchers from public or private laboratories. It concerns both theoretical and practical aspects of KDM. The book is structured in two parts called "Applications of KDM to real datasets" and "Foundations of KDM". .