Agents and Data Mining Interaction

10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers
Herausgegeben:Cao, Longbing; Zeng, Yifeng; An, Bo; Symeonidis, Andreas L.; Gorodetsky, Vladimir; Coenen, Frans; Yu, Philip S

• Broschiertes Buch

Produktbeschreibung

This book constitutes the thoroughly refereed and revised selected papers from the 10th International Workshop on Agents and Data Mining Interactions, ADMI 2014, held in Paris, France, in May 2014 as satellite workshop of AAMAS 2014, the 13th International Conference on Autonomous Agents and Multiagent Systems.

The 11 papers presented were carefully reviewed and selected from numerous submissions for inclusion in this volume. They present current research and engineering results, as well as potential challenges and prospects encountered in the respective communities and the coupling between agents and data mining.

Produktdetails

• Lecture Notes in Computer Science 9145
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-20229-7
• 2015
• Seitenzahl: 140
• Erscheinungstermin: 25. Juni 2015
• Englisch
• Abmessung: 235mm x 155mm x 8mm
• Gewicht: 225g
• ISBN-13: 9783319202297
• ISBN-10: 3319202294
• Artikelnr.: 42753758

Inhaltsangabe

Learning Agents' Relations in Interactive Multiagent Dynamic Influence Diagrams.- Agent-Based Customer Profile Learning in 3G Recommender Systems: Ontology-Driven Multi-source Cross-Domain Case.- Modeling Temporal Propagation Dynamics in Multiplex Networks.- Mining Movement Patterns from Video Data to Inform Multi-agent Based Simulation.- Accessory-Based Multi-agent Simulating Platform on the Web.- Performance Evaluation of Agents and Multi-agent Systems Using Formal Specifications in Z Notation.- Reputation in Communities of Agent-Based Web Services Through Data Mining.- Data Mining Process Optimization in Computational Multi-agent Systems.- Diversifying the Storytelling Using Bayesian Networks.- A Coupled Similarity Kernel for Pairwise Support Vector Machine.

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Learning Agents’ Relations in Interactive Multiagent Dynamic Influence Diagrams.- Agent-Based Customer Profile Learning in 3G Recommender Systems: Ontology-Driven Multi-source Cross-Domain Case.- Modeling Temporal Propagation Dynamics in Multiplex Networks.- Mining Movement Patterns from Video Data to Inform Multi-agent Based Simulation.- Accessory-Based Multi-agent Simulating Platform on the Web.- Performance Evaluation of Agents and Multi-agent Systems Using Formal Specifications in Z Notation.- Reputation in Communities of Agent-Based Web Services Through Data Mining.- Data Mining Process Optimization in Computational Multi-agent Systems.- Diversifying the Storytelling Using Bayesian Networks.- A Coupled Similarity Kernel for Pairwise Support Vector Machine.