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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases: 98 (Studies in Computational Intelligence) - Tapa blanda
Sinopsis
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.
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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.
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence, 98)
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Multi-objective Evolutionary Algorithms for Knowledge Discovery from Databases
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence, 98)
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Paperback)
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Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783642096150
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence, 98)
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 176 pp. Englisch. Nº de ref. del artículo: 9783642096150
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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
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Springer Berlin Heidelberg, 2010
ISBN 10: 3642096158
ISBN 13: 9783642096150
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Assembles high quality original contributions that reflect and advance the state-of-the art in the area of Multi-objective Evolutionary Algorithms for Data Mining and Knowledge DiscoveryEmphasizes on the utility of evolutionary algorithms to vario. Nº de ref. del artículo: 5048619
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