Intelligent Technologies for Information Analysis

1. Front Matter

   Pages I-XXVII

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2. The Alchemy of Intelligent IT (iIT): A Blueprint for Future Information Technology

   1. The Alchemy of Intelligent IT (iIT): A Blueprint for Future Information Technology

      • Ning Zhong, Jiming Liu

      Pages 1-16

3. Emerging Data Mining Technology

   1. Front Matter

      Pages 17-17

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   2. Grid-Based Data Mining and Knowledge Discovery

      • Mario Cannataro, Antonio Congiusta, Carlo Mastroianni, Andrea Pugliese, Domenico Talia, Paolo Trunfio

      Pages 19-45

   3. The MiningMart Approach to Knowledge Discovery in Databases

      • Katharina Morik, Martin Scholz

      Pages 47-65

   4. Ensemble Methods and Rule Generation

      • Yongdai Kim, Jinseog Kim, Jongwoo Jeon

      Pages 67-87

   5. Evaluation Scheme for Exception Rule/Group Discovery

      • Einoshin Suzuki

      Pages 89-108

   6. Data Mining for Targeted Marketing

      • Ning Zhong, Yiyu Yao, Chunnian Liu, Jiajin Huang, Chuangxin Ou

      Pages 109-131

4. Data Mining for Web Intelligence

   1. Front Matter

      Pages 133-133

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   2. Mining for Information Discovery on the Web: Overview and Illustrative Research

      • Hwanjo Yu, AnHai Doan, Jiawei Han

      Pages 135-168

   3. Mining Web Logs for Actionable Knowledge

      • Qiang Yang, Charles X. Ling, Jianfeng Gao

      Pages 169-191

   4. Discovery of Web Robot Sessions Based on Their Navigational Patterns

      • Pang-Ning Tan, Vipin Kumar

      Pages 193-222

   5. Web Ontology Learning and Engineering: An Integrated Approach

      • Roberto Navigli, Paola Velardi, Michele Missikoff

      Pages 223-242

   6. Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis

      • Richard Cole, Florence Amardeilh, Peter Eklund

      Pages 243-264

   7. Graph Discovery and Visualization from Textual Data

      • Vincent Dubois, Mohamed Quafafou

      Pages 265-288

5. Emerging Agent Technology

   1. Front Matter

      Pages 289-289

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   2. Agent Networks: Topological and Clustering Characterization

      • Xiaolong Jin, Jiming Liu

      Pages 291-310

   3. Finding the Best Agents for Cooperation

      • Francesco Buccafurri, Luigi Palopoli, Domenico Rosaci, Giuseppe M. L. Sarnè

      Pages 311-332

   4. Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives

      • Zili Zhang, Chengqi Zhang

      Pages 333-359

   5. Making Agents Acceptable to People

      • Jeffrey M. Bradshaw, Patrick Beautement, Maggie R. Breedy, Larry Bunch, Sergey V. Drakunov, Paul J. Feltovich et al.

      Pages 361-406

Intelligent Information Technology (iiT) encompasses the theories and ap­ plications of artificial intelligence, statistical pattern recognition, learning theory, data warehousing, data mining and knowledge discovery, Grid com­ puting, and autonomous agents and multi-agent systems in the context of today's as well as future IT, such as Electronic Commerce (EC), Business Intelligence (BI), Social Intelligence (SI), Web Intelligence (WI), Knowledge Grid (KG), and Knowledge Community (KC), among others. The multi-author monograph presents the current state of the research and development in intelligent technologies for information analysis, in par­ ticular, advances in agents, data mining, and learning theory, from both the­ oretical and application aspects. It investigates the future of information technology (IT) from a new intelligent IT (iiT) perspective, and highlights major iiT-related topics by structuring an introductory chapter and 22 sur­ vey/research chapters into 5 parts: (1) emerging data mining technology, (2) data mining for Web intelligence, (3) emerging agent technology, ( 4) emerging soft computing technology, and (5) statistical learning theory. Each chapter includes the original work of the author(s) as well as a comprehensive survey related to the chapter's topic. This book will become a valuable source of reference for R&D profession­ als active in advanced intelligent information technologies. Students as well as IT professionals and ambitious practitioners concerned with advanced in­ telligent information technologies will appreciate the book as a useful text enhanced by numerous illustrations and examples.

• STATISTICA
• Web Intelligence
• agents
• architectures
• artificial intelligence
• calculus
• cognition
• data mining
• intelligence
• knowledge
• learning
• learning theory
• pattern recognition
• problem solving
• sets

• Department of Information Engineering, Maebashi Institute of Technology, Maebashi, Japan

  Ning Zhong

• Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

  Jiming Liu

 

Ning Zhong is currently head of Knowledge Information Systems Laboratory, and a professor in Department of Systems and Information Engineering, Graduate School, Maebashi Institute of Technology, Japan. He is also CEO of Web Intelligence Laboratory, Inc., a new type of venture intelligent IT business company. Before moving to Maebashi Institute of Technology, he was an associate professor in Department of Computer Science and Systems Engineering, Yamaguchi University, Japan. He is also a guest professor of Beijing University of Technology since 1998. He is the co-founder and co-chair of Web Intelligence Consortium (WIC), vice chair of the executive committee of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI), the advisory board of ACM SIGART, steering committee of IEEE International Conferences on Data Mining (ICDM), the advisory board of International Rough Set Society, steering committee of Pacific-Asia Conferences on Knowledge Discovery and Data Mining (PAKDD), coordinator and member of advisory board of a Special Interest Group on Granular Computing in Berkeley Initiative in Soft Computing (BISC/SIG-GrC). Even more information can be found on his home page http://www.kis-lab.com/zhong/

 

Dr. Jiming Liu is the Head of Computer Science Department at Hong Kong Baptist University (HKBU). He leads the AAMAS/AOC Research Group (i.e., Autonomous Agents and Multi-Agent Systems / Autonomy-Oriented Computing) at HKBU. He holds a B.Sc. degree in Physics from East China Normal University in Shanghai, an M.A. degree in Educational Technology from Concordia University in Montreal, and an M.Eng. and a Ph.D. degrees both in Electrical Engineering from McGill University in Montreal. In Feb.-July 1999, Dr. Liu was an invited Visiting Scholar in Computer Science Department, Stanford University, where he was associated with the AI & Robotics Laboratory and taught advanced graduate classes on topicsrelated to Robot Learning, Neural Robots, and Evolutionary Robotics. He is Guest Professor at University of Science and Technology of China, East China Normal University (Software Engineering Institute), and Beijing University of Technology, as well as Adjunct Fellow at E-Business Technology Institute (ETI - a joint partnership institute between IBM and University of Hong Kong). Dr. Liu is the co-founder of Web Intelligence Consortium (WIC), an international organization dedicated to promoting world-wide scientific research and industrial development in the era of Web and agent Intelligence. He has founded andserved, or is serving, as Program, Conference, Workshop, and General Chairs for several international conferences and workshops, including The IEEE/WIC International Conference on Web Intelligence (WI) series and The IEEE/WIC International Conference on Intelligent Agent Technology (IAT) series, and is presently serving as the Senior Program Committee Member, Program Committee Member, and Steering/Planning Committee Member for many major international conferences.

 

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