Rough Sets: Selected Methods and Applications in Management and Engineering / edited by Georg Peters, Pawan Lingras, Dominik Ślęzak, Yiyu Yao.

Format:

Book

Contributor:

Peters, Georg, editor.

Lingras, Pawan, editor.

Ślęzak, Dominik, editor.

Yao, Yiyu, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advanced information and knowledge processing 1610-3947

Advanced Information and Knowledge Processing, 1610-3947

Language:

English

Subjects (All):

Artificial intelligence.

Application software.

Artificial Intelligence.

Computer Appl. in Administrative Data Processing.

Local Subjects:

Artificial Intelligence.

Computer Appl. in Administrative Data Processing.

Physical Description:

1 online resource (X, 214 pages) : 130 illustrations, 86 illustrations in color.

Edition:

First edition 2012.

Contained In:

Springer eBooks

Place of Publication:

London : Springer London : Imprint: Springer, 2012.

System Details:

text file PDF

Summary:

Rough Set Theory was introduced in the early 1980's. In the last quarter century it has become an important part of soft computing and has proved its relevance in many real-world applications. Initially most articles on Rough Sets were centered on theory, currently though the focus of the research has shifted to practical usage of mathematical advances. With this in mind this book is written for researchers at universities wanting to use Rough Sets to solve real-world problems and needing guidance on how best to describe their ideas in ways not only understandable to industry readers, but also for managers looking for methods to improve their businesses, and researchers in industrial laboratories and think-tanks investigating new methods to enhance the efficiency of their solutions. Rough Sets: Selected Methods and Applications in Management and Engineering is unique in its focus on use cases backed by sound theory in contrast to the presentation of a theory applied to a problem. A diverse range of applications, including coverage of methods in data analysis, decision support as well as management and engineering, demonstrates the great potential of Rough Sets in almost any domain.

Contents:

Preface

Contributors

Part I: Foundations of Rough Sets

An Introduction to Rough Sets

Part II: Methods and Applications in Data Analysis

Applying Rough Set Concepts to Clustering

Rough Clustering Approaches for Dynamic Environments

Feature Selection, Classification and Rule Generation using Rough Sets

Part III: Methods and Applications in Decision Support

Three-way Decisions using Rough Sets

Rough Set Based Decision Support - Models East to Interpret

Part IV: Methods and Applications in Management

Financial Series Forecasting using Dual Rough Support Vector Regression

Grounding Information Technology Project Critical Success Factors within the Organization

Workflow Management supported by Rough Set Concepts

Part V: Methods and Applications in Engineering

Rough Natural Hazards Monitoring

Nearness of Associated Rough Sets

Contributor's Biography

Index.

Other Format:

Printed edition:

ISBN:

978-1-4471-2760-4

9781447127604

Access Restriction:

Restricted for use by site license.