TY - BOOK ID - 5454167 TI - Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases AU - Ghosh, Ashish AU - Dehuri, Satchidananda AU - Ghosh, Susmita PY - 2008 VL - v. 98 SN - 1860949X SN - 9783540774662 3540774661 354077467X PB - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, DB - UniCat KW - Data mining KW - Evolutionary computation KW - Exploration de données (Informatique) KW - Réseaux neuronaux à structure évolutive KW - Computer Science KW - Applied Mathematics KW - Civil Engineering KW - Engineering & Applied Sciences KW - Civil & Environmental Engineering KW - Data mining. KW - Evolutionary computation. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Computation, Evolutionary KW - Engineering. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Engineering KW - Engineering analysis KW - Mathematical 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 - 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 - Construction KW - Industrial arts KW - Technology KW - Mathematics KW - Neural networks (Computer science) KW - Database searching KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. KW - Engineering—Data processing. KW - Mathematical and Computational Engineering Applications. UR - https://www.unicat.be/uniCat?func=search&query=sysid:5454167 AB - Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM. The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases. ER -