Rough Set Theory : A True Landmark in Data Analysis / Ajith Abraham, Rafael Falcón, Rafael Bello editors

Contributor(s):

Material type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9783540899204

Subject(s):

DDC classification:

23 511.322 ABR

Summary: Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.

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Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Gifted Books	Sai University Library General stacks				511.322 ABR (Browse shelf(Opens below))					Available			G2978

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Previous	511.3 KOZ Automata and computability	511.3 MAT Elementary logic	511.3 WAL Everything and more : a compact history of infinity	511.322 ABR Rough Set Theory : A True Landmark in Data Analysis /	511.35 HOP Introduction to automata theory, languages, and computation	511.35 SIP Introduction to the theory of computation	511.4 ABR Foundations of Computational Intelligence. Volume 5 : Function Approximation and Classification /	Next

Includes index

Gifted by Ajith Abraham

Includes bibliographical references and index

Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.

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