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Intelligent Technologies for Information Analysis

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Titel: Intelligent Technologies for Information Analysis

ISBN: 3540406778
EAN: 9783540406778
Auflage 2004.
HC runder Rücken kaschiert.
Sprache: Englisch.
Herausgegeben von Jiming Liu, Ning Zhong
Springer Berlin Heidelberg

12. August 2004 - gebunden - 740 Seiten

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.
1) The Alchemy of Intelligent IT (iIT) (Ning Zhong, Jiming Liu) Part I Emerging Data Mining Technology ======================================= 2) Grid-Based Data Mining and Knowledge Discovery (Mario Cannataro, Antonio Congiusta, Carlo Mastroianni, Andrea Pugliese, Domenico Talia, Paolo Trunfio) 3) The MiningMart Approach to Knowledge Discovery in Databases (Katharina Morik, Martin Scholz) 4) Ensemble Methods and Rule Generation (Yongdai Kim, Jinseog Kim, Jongwoo Jeon) 5) Evaluation Scheme for Exception Rule/Group Discovery (Einoshin Suzuki) 6) Data Mining for Direct Marketing (Ning Zhong, Yiyu Yao, Chunnian Liu, Chuangxin Ou, Jiajin Huang) Part II Data Mining for Web Intelligence ========================================= 7) Mining for Information Discovery on the Web (Hwanjo Yu, An Hai Doan, Jiawei Han) 8) Mining Web Logs for Actionable Knowledge (Qiang Yang, Charles X. Ling, Jianfeng Gao) 9) Discovery of Web Robot Sessions Based on Their Navigational Patterns (Pang-Ning Tan, Vipin Kumar) 10) Web Ontology Learning and Engineering (Roberto Navigli, Paola Velardi, Michele Missikoff) 11) Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis (Richard Cole, Peter Eklund, Florence Amardeilh) 12) Graph Discovery and Visualization from Textual Data (Vincent Dubois, Mohamed Quafafou) Part III Emerging Agent Technology =================================== 13) Agent Networks: Topological and Clustering Characterization (Xiaolong Jin, Jiming Liu) 14) Finding the Best Agents for Cooperation (Francesco Buccafurri, Domenico Rosaci, Giuseppe L.M. Sarne, Luigi Palopoli) 15) Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives (Zili Zhang, Zhengqi Zhang) 16) Making Agents Acceptable to People (Jeffrey M. Bradshaw, Patrick Beautement, Maggie R. Breedy, Larry Bunch, Sergey V. Drakunov, Paul J. Feltovich, Robert R. Hoffman, Renia Jeffers, Matthew Johnson, Shriniwas Kulkarnt, James Lott, Anil K. Raj, Niranjan Suri, Andrzej Uszok) Part IV Emerging Soft Computing Technology =========================================== 17) Constraint-Based Neural Network Learning for Time Series Predictions (Benjamin W. Wah, Minglun Qian) 18) Approximate Reasoning in Distributed Environments (Andrzej Skowron) 19) Soft Computing Pattern Recognition, Data Mining, and Web Intelligence (Sankar K. Pal, Sushmita Mitra, Pabitra Mitra) 20) Dominance-Based Rough Set Approach to Knowledge Discovery (I) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) 21) Dominance-Based Rough Set Approach to Knowledge Discovery (II) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) Part V Statistical Learning Theory =================================== 22) Mining Dependence Structures (I) -- A General Statistical Learning Perspective -- (Lei Xu) 23) Mining Dependence Strucutres (II) -- An Independence Analysis Perspective -- (Lei Xu)
 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 topics related 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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