TY - BOOK ID - 5451696 TI - Foundations and Novel Approaches in Data Mining AU - Lin, Tsau Young. AU - Ohsuga, Setsuo. AU - Liau, Churn-Jung. AU - Hu, Xiaohua. PY - 2006 SN - 9783540283157 3540283153 3540312293 PB - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, DB - UniCat KW - Data mining KW - Exploration de données (Informatique) KW - Congresses. KW - Congrès KW - Engineering. KW - Artificial intelligence. KW - Engineering mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Civil Engineering KW - Applied Mathematics KW - Computer Science KW - Engineering & Applied Sciences KW - Civil & Environmental Engineering KW - Engineering KW - Engineering analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Construction KW - Mathematics KW - Applied mathematics. KW - Mathematical analysis KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Industrial arts KW - Technology KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:5451696 AB - Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor” syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for realworld problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics. ER -