menu
{ "item_title" : "Rough Set Methods and Applications", "item_author" : [" Lech Polkowski", "Shusaku Tsumoto", "Tsau Y. Lin "], "item_description" : "Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/66/200/376/3662003767_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Rough Set Methods and Applications|Lech Polkowski

Rough Set Methods and Applications : New Developments in Knowledge Discovery in Information Systems

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.

This item is Non-Returnable

Details

  • ISBN-13: 9783662003763
  • ISBN-10: 3662003767
  • Publisher: Physica-Verlag
  • Publish Date: October 2012
  • Dimensions: 9.21 x 6.14 x 1.39 inches
  • Shipping Weight: 2.11 pounds
  • Page Count: 683

Related Categories

You May Also Like...

    1

BAM Customer Reviews