Overview
- 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 Discovery
- Emphasizes on the utility of evolutionary algorithms to various facets of Knowledge Discovery in Databases that involve multiple objectives
Part of the book series: Studies in Computational Intelligence (SCI, volume 98)
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Table of contents (7 chapters)
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Front Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Editors: Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-77467-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Hardcover ISBN: 978-3-540-77466-2Published: 19 March 2008
Softcover ISBN: 978-3-642-09615-0Published: 19 November 2010
eBook ISBN: 978-3-540-77467-9Published: 28 February 2008
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 162
Topics: Mathematical and Computational Engineering, Artificial Intelligence