Advances in knowledge discovery and management. Volume 5 ([2015])

• Record Type: Book
• Further Information: Note: Fabrice Guillet [and three others] editors.
• Editors: Guillet, Fabrice
• Other Names: EGC (Conference), 13th; EGC (Conference), 14th
• Contents: 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 Graphs2.2 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 Categorization5 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 Predictions1 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
• Publisher Details: Cham : Springer
• Publication Date: 2015
• Copyright Date: 2016
• Extent: 1 online resource (xviii, 137 pages), illustrations (black and white, and color)
• Subjects: 006.3/12; Engineering; Data mining -- Congresses; Knowledge management -- Congresses; Data mining; Knowledge management; Computers -- Intelligence (AI) & Semantics; Business & Economics -- Operations Research; Artificial intelligence; Operational research; Artificial intelligence; Operations research; Conference papers and proceedings; Electronic books
• Languages: English
• ISBNs: 9783319237510; 3319237519
• Related ISBNs: 9783319237503
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed September 28, 2015).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.