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NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING

NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING

NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING
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NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING Hardback - 2010 - 1st Edition

by WANG

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  • Title NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING
  • Author WANG
  • Binding Hardback
  • Edition number 1st
  • Edition 1
  • Condition New
  • Pages 360
  • Volumes 1
  • Language ENG
  • Publisher World Scientific Publishing Company, U.S.A
  • Publication date 2010-09-30
  • Features Bibliography
  • Bookseller's Inventory # 9789812814678
  • ISBN 9789812814678 / 9812814671
  • Weight 1.45 lbs (0.66 kg)
  • Dimensions 9 x 6.1 x 0.9 in (22.86 x 15.49 x 2.29 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Mathematics
  • Dewey Decimal Code 511.313
  • Quantity available 178

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Reader reviews for NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING

From the publisher

Regarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be treated and fuzzy information is retrievable.The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.

From the jacket flap

Regarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be treated and fuzzy information is retrievable.

The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.

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