Intelligent technologies for information analysis
著者
書誌事項
Intelligent technologies for information analysis
Springer, c2004
大学図書館所蔵 全11件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical reference and index
内容説明・目次
内容説明
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.
目次
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)
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