Agents and Data Mining Interaction

1. Front Matter

   Pages I-IX

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2. Agent Mining

   1. Front Matter

      Pages 1-1

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   2. Using Dynamic Bayesian Networks to Model User-Experience

      • Paul van Schaik, Yifeng Zeng, Iain Spears

      Pages 3-13

   3. Multi-Agent Joint Learning from Argumentation

      • Junyi Xu, Li Yao, Le Li, Jinyang Li

      Pages 14-25

   4. Towards Mining Norms in Open Source Software Repositories

      • Bastin Tony Roy Savarimuthu, Hoa Khanh Dam

      Pages 26-39

   5. The Recognition of Multiple Virtual Identities Association Based on Multi-agent System

      • Le Li, Weidong Xiao, Changhua Dai, Junyi Xu, Bin Ge

      Pages 40-50

3. Data Mining

   1. Front Matter

      Pages 51-51

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   2. Redundant Feature Selection for Telemetry Data

      • Phillip Taylor, Nathan Griffths, Abhir Bhalerao, Thomas Popham, Xu Zhou, Alain Dunoyer

      Pages 53-65

   3. Mining Emerging Patterns of PIU from Computer-Mediated Interaction Events

      • Yaxin Yu, Ke Yan, Xinhua Zhu, Guoren Wang, Dan Luo, Suresh Sood

      Pages 66-78

   4. Learning Heterogeneous Coupling Relationships Between Non-IID Terms

      • Mu Li, Jinjiu Li, Yuming Ou, Ya Zhang, Dan Luo, Maninder Bahtia et al.

      Pages 79-91

   5. Learning the Hotness of Information Diffusions with Multi-dimensional Hawkes Processes

      • Yi Wei, Ke Zhou, Ya Zhang, Hongyuan Zha

      Pages 92-110

   6. A Spectral Clustering Algorithm Based on Hierarchical Method

      • Xiwei Chen, Li Liu, Dashi Luo, Guandong Xu, Yonggang Lu, Ming Liu et al.

      Pages 111-123

   7. Transitive Identity Mapping Using Force-Based Clustering

      • H. Van Dyke Parunak, Sven Brueckner

      Pages 124-135

4. Back Matter

   Pages 137-137

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This book constitutes the thoroughly refereed and revised selected papers from the 9th International Workshop on Agents and Data Mining Interaction, ADMI 2013, held in Saint Paul, MN, USA in May 2013. The 10 papers presented in this volume were carefully selected for inclusion in the book and are organized in topical sections named agent mining and data mining.

• agent mining
• artificial intelligence
• clustering
• data mining
• distributed systems
• intelligent agents
• machine learning
• multi-agent learning
• multi-agent systems

• University of Technology Sydney, Sydney, Australia

  Longbing Cao

• Teesside University, Middlesbrough, United Kingdom

  Yifeng Zeng

• Aristotle University of Thessaloniki, Thessaloniki, Greece

  Andreas L. Symeonidis

• St. Petersburg Institute for Informatics, St. Petersburg, Russia

  Vladimir Gorodetsky

• Department of Informatics, Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

  Jörg P. Müller

• University of Illinois Chicago, Chicago, USA

  Philip S. Yu

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