Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence, 98, Band 98)

Ghosh, Ashish (Hrsg.), Dehuri, Satchidananda (Hrsg.), Ghosh, Susmita (Hrsg.)

ISBN 10: 3540774661 / ISBN 13: 9783540774662

Editore: Springer, 2008

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Titolo: Multi-Objective Evolutionary Algorithms for ...

Casa editrice: Springer

Data di pubblicazione: 2008

Legatura: Hardcover

Condizione: New

autografato: nein

Descrizione articolo

Riassunto:

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.

Dalla quarta di copertina:

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.