LEADER 07088nam 22007455 450 001 9910254189303321 005 20200704181847.0 010 $a3-319-23751-9 024 7 $a10.1007/978-3-319-23751-0 035 $a(CKB)3780000000094056 035 $a(SSID)ssj0001584546 035 $a(PQKBManifestationID)16264254 035 $a(PQKBTitleCode)TC0001584546 035 $a(PQKBWorkID)14865889 035 $a(PQKB)11790460 035 $a(DE-He213)978-3-319-23751-0 035 $a(MiAaPQ)EBC6281818 035 $a(MiAaPQ)EBC5595274 035 $a(Au-PeEL)EBL5595274 035 $a(OCoLC)922340468 035 $a(PPN)190531274 035 $a(EXLCZ)993780000000094056 100 $a20150925d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Knowledge Discovery and Management $eVolume 5 /$fedited by Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XVIII, 137 p. 40 illus., 29 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v615 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-23750-0 327 $aIntro -- 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. 327 $aDynamic 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 ?-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. 330 $aThis book is a collection of representative and novel works done in Data Mining, Knowledge Discovery, Clustering and Classification that were originally presented in French at the EGC'2013 (Toulouse, France, January 2013) and EGC'2014 Conferences (Rennes, France, January 2014). These conferences were respectively the 13th and 14th editions of this event, which takes place each year and which is now successful and well-known in the French-speaking community. This community was structured in 2003 by the foundation of the French-speaking EGC society (EGC in French stands for "Extraction et Gestion des Connaissances" and means "Knowledge Discovery and Management", or KDM). This book is aiming at all researchers interested in these fields, including PhD or MSc students, and researchers from public or private laboratories. It concerns both theoretical and practical aspects of KDM. The book is structured in two parts called "Applications of KDM to real datasets" and "Foundations of KDM".  . 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v615 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aOperations research 606 $aDecision making 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aOperations research. 615 0$aDecision making. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aOperations Research/Decision Theory. 676 $a006.3 702 $aGuillet$b Fabrice$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPinaud$b Bruno$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVenturini$b Gilles$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZighed$b Djamel Abdelkader$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254189303321 996 $aAdvances in Knowledge Discovery and Management$91540610 997 $aUNINA