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{ "item_title" : "Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases", "item_author" : [" Ashish Ghosh", "Satchidananda Dehuri", "Susmita Ghosh "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/54/077/466/3540774661_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases|Ashish Ghosh

Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

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Overview

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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Details

  • ISBN-13: 9783540774662
  • ISBN-10: 3540774661
  • Publisher: Springer
  • Publish Date: March 2008
  • Dimensions: 9.21 x 6.14 x 0.44 inches
  • Shipping Weight: 0.94 pounds
  • Page Count: 162

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