Stock photo: cover may vary
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence) Paperback - 2008
by Dehuri, Satchidananda (Editor) / Ghosh, Ashish (Editor) / Ghosh, Susmita (Editor)
- New
- Paperback
A$249.28
A$28.98
Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Standard delivery: 7 to 14 days
Ships from Revaluation Books (Devon, United Kingdom)
Details
- Title Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence)
- Author Dehuri, Satchidananda (Editor) / Ghosh, Ashish (Editor) / Ghosh, Susmita (Editor)
- Binding Paperback
- Edition Softcover reprin
- Condition New
- Pages 162
- Volumes 1
- Language ENG
- Publisher Springer Berlin Heidelberg
- Publication date 2008
- Bookseller's Inventory # x-3642096158
- ISBN 9783642096150 / 3642096158
- Weight 0.56 lbs (0.25 kg)
- Dimensions 9.21 x 6.14 x 0.38 in (23.39 x 15.60 x 0.97 cm)
- Category Mathematics
- Dewey Decimal Code 006.312
- Quantity available 2
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
Reader reviews for Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence)
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the rear cover
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.
