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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Name: Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 01569746
Price: 37337 HUF
Availability: InStock
Author: Ashish Ghosh
ISBN: 9783540774662

Ashish Ghosh

Satchidananda Dehuri

Susmita Ghosh

Language

English

Book Hardback

Libristo code: 01569746

Publishers Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, March 2008

Data Mining (DM) is the most commonly used name to describe such computational analysis of data and... Full description

Libristo code: 01569746

249 b

37 337 Ft

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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.