Transactions on Rough Sets IX

1. Front Matter

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2. Vagueness and Roughness

   • Zbigniew Bonikowski, Urszula Wybraniec-Skardowska

   Pages 1-13

3. Modified Indiscernibility Relation in the Theory of Rough Sets with Real-Valued Attributes: Application to Recognition of Fraunhofer Diffraction Patterns

   • Krzysztof A. Cyran

   Pages 14-34

4. On Certain Rough Inclusion Functions

   • Anna Gomolińska

   Pages 35-55

5. Automatic Rhythm Retrieval from Musical Files

   • Bożena Kostek, Jarosław Wójcik, Piotr Szczuko

   Pages 56-75

6. FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute

   • Marzena Kryszkiewicz, Piotr Lasek

   Pages 76-95

7. Information Granulation: A Medical Case Study

   • Urszula Kużelewska, Jarosław Stepaniuk

   Pages 96-113

8. Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

   • Pradipta Maji, Sankar K. Pal

   Pages 114-134

9. Approximation Schemes in Logic and Artificial Intelligence

   • Victor W. Marek, Mirosław Truszczyński

   Pages 135-144

10. Decision Rule Based Data Models Using NetTRS System Overview

    • Marcin Michalak, Marek Sikora

    Pages 145-156

11. A Rough Set Based Approach for ECG Classification

    • Sucharita Mitra, M. Mitra, B. B. Chaudhuri

    Pages 157-186

12. Universal Problem of Attribute Reduction

    • Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko

    Pages 187-199

13. Extracting Relevant Information about Reduct Sets from Data Tables

    • Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

    Pages 200-211

14. Context Algebras, Context Frames, and Their Discrete Duality

    • Ewa Orłowska, Ingrid Rewitzky

    Pages 212-229

15. A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data

    • Lech Polkowski, Piotr Artiemjew

    Pages 230-263

16. On Classifying Mappings Induced by Granular Structures

    • Lech Polkowski, Piotr Artiemjew

    Pages 264-286

17. The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory

    • Andrzej W. Przybyszewski

    Pages 287-317

18. Diagnostic Feature Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets

    • Kenneth Revett

    Pages 318-327

19. Rules and Apriori Algorithm in Non-deterministic Information Systems

    • Hiroshi Sakai, Ryuji Ishibashi, Kazuhiro Koba, Michinori Nakata

    Pages 328-350

20. On Extension of Dependency and Consistency Degrees of Two Knowledges Represented by Covering

    • P. Samanta, Mihir K. Chakraborty

    Pages 351-364

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence.

This book, which constitutes the ninth volume of the Transactions on Rough Sets series, providing evidence of the continuing growth of a number of research streams. It includes articles that are extensions of papers included in the first conference on Rough Sets and Intelligent Systems Paradigms.

The 26 papers presented in this volume introduce a number of new advances in the foundations and applications of artificial intelligence, engineering, image processing, logic, mathematics, medicine, music, and science.  

• Action
• Extension
• artificial intelligence
• data mining
• image processing
• knowledge
• knowledge discovery

From the reviews: “Pawlak originally developed rough sets to be used in the same situations as Zadeh’s better-known fuzzy sets … . Although they are less popular than fuzzy sets, they do have a devoted following and are proven to be a good tool when properly applied. … the quality and significance of the contributions vary considerably. … the book emphasizes that rough set theory is a viable tool in artificial intelligence and other areas of data handling … which makes it worth keeping in one’s mental toolbox.” (Jonathan Golan, ACM Computing Reviews, December, 2009)

• Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada

  James F. Peters

• Institute of Mathematics, Warsaw University, Warsaw, Poland

  Andrzej Skowron

• Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

  Henryk Rybiński

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