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© 2009

Inhibitory Rules in Data Analysis

A Rough Set Approach

  • Authors
Book

Part of the Studies in Computational Intelligence book series (SCI, volume 163)

Table of contents

  1. Front Matter
  2. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 1-8
  3. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 9-29
  4. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 31-41
  5. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 43-62
  6. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 63-79
  7. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 81-86
  8. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 87-97
  9. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 99-106
  10. Pawel Delimata, Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj
    Pages 107-108
  11. Back Matter

About this book

Introduction

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 does not equal value". The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely information encoded in decision or information systems and to design classifiers of high quality.

The most important feature of this monograph is that it includes an advanced 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 classifiers 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.

Keywords

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

Bibliographic information

Reviews

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)