Inhibitory Rules in Data Analysis

1. Front Matter

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2. Introduction

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 1-8

3. Maximal Consistent Extensions of Information Systems

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 9-29

4. Minimal Inhibitory Association Rules for Almost All k-Valued Information Systems

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 31-41

5. Partial Covers and Inhibitory Decision Rules

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 43-62

6. Partial Covers and Inhibitory Decision Rules with Weights

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 63-79

7. Classifiers Based on Deterministic and Inhibitory Decision Rules

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 81-86

8. Lazy Classification Algorithms Based on Deterministic and Inhibitory Association Rules

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 87-97

9. Lazy Classification Algorithms Based on Deterministic and Inhibitory Decision Rules

   • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

   Pages 99-106

10. Final Remarks

    • Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj

    Pages 107-108

11. Back Matter

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This monograph is devoted to theoretical and experimental study of inhibitory decision and association rules. Inhibitory rules contain on the right-hand side a relation of the kind “attribut = value”. The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely infor- tion encoded in decision or information systems and to design classi?ers of high quality. The mostimportantfeatureofthis monographis thatit includesanadvanced mathematical analysis of problems on inhibitory rules. We consider algorithms for construction of inhibitory rules, bounds on minimal complexity of inhibitory rules, and algorithms for construction of the set of all minimal inhibitory rules. We also discuss results of experiments with standard and lazy classi?ers based on inhibitory rules. These results show that inhibitory decision and association rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction. Inhibitory rules can be also used under the analysis and design of concurrent systems. The results obtained in the monograph can be useful for researchers in such areas as machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory, and logical analysis of data (LAD). The monograph can be used under the creation of courses for graduate students and for Ph.D. studies. TheauthorsofthisbookextendanexpressionofgratitudetoProfessorJanusz Kacprzyk, to Dr. Thomas Ditzinger and to the Studies in Computational Int- ligence sta? at Springer for their support in making this book possible.

• Computational Intelligence
• Data Analysis
• Extension
• Inhibitory Rules
• Rough Sets
• algorithm
• algorithms
• calculus
• classification
• information system

From the reviews:

"This monograph is devoted to the theoretical and experimental study of decision and association rules. The most interesting part of the book is that it discusses an advanced mathematical analysis of problems and its rules. … I am sure that this book will be very useful to researchers in the area of data mining and the analysis and design of concurrent systems. It will be useful for PhD students in their very first year of study." (Prabhat Kumar Mahanti, Zentralblatt MATH, Vol. 1157, 2009)

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