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.

Zusammenfassung

Rough Set Theory, introduced by Pawlak in the early 1980s, has become an important part of soft computing within the last 25 years. However, much of the focus has been on the theoretical understanding of Rough Sets, with a survey of Rough Sets and their applications within business and industry much desired. Rough Sets: Selected Methods and Applications in Management and Engineering provides context to Rough Set theory, with each chapter exploring a real-world application of Rough Sets.

Rough Sets is relevant to managers striving to improve their businesses, industry researchers looking to improve the efficiency of their solutions, and university researchers wanting to apply Rough Sets to real-world problems.

Inhalt

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.