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Nonlinear integrals and their applications in data mining (Advances in Fuzzy Systemss - Applications and Theory, 24)

Nonlinear integrals and their applications in data mining (Advances in Fuzzy Systemss - Applications and Theory, 24)

Nonlinear integrals and their applications in data mining (Advances in Fuzzy
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Nonlinear integrals and their applications in data mining (Advances in Fuzzy Systemss - Applications and Theory, 24) Paperback - 2010 - 1st Edition

by Wang, Zhenyuan

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Wspc, 6/9/2010 12:00:01 AM. paperback. Very Good. 1.0000 in x 9.0000 in x 6.1000 in.
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Reader reviews for Nonlinear integrals and their applications in data mining (Advances in Fuzzy Systemss - Applications and Theory, 24)

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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