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.
| ISBN: | 9783540774662 |
| Publication date: | 19th March 2008 |
| Author: | Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Hardback |
| Pagination: | 162 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Maths for engineers Artificial intelligence |
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.
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases features in the following genres: Maths for engineers, Artificial intelligence
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases is available in Hardback
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases was written by Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh and published by Springer an imprint of Springer Berlin Heidelberg
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases has 162 pages
Yes it is part of Studies in Computational Intelligence series