Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems

604 Pages·1998·22.48 MB·English

by  Lech Polkowski

Rough Sets in
Knowledge Discovery 2
Studies in Fuzziness and Soft Computing
Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw, Poland
E-mail: [email protected]
Vol. 3. A. Geyer-Schulz Vol. 14. E. Hisdal
Fuzzy Rule-Based Expert Systems and Genetic Logical Structures for Representation of
Machine Learning, 2nd ed. i996 Knowledge and Uncertainty, i998
ISBN 3-7908-0964-0 ISBN 3-7908-1056-8
Vol. 4. T. Onisawa and J. Kacprzyk (Eds.) Vol. 15. G.J. Klir and M.J. Wierman
Reliability and Safety Analyses under Uncertainty-Based Information, i998
Fuzziness, i995 ISBN 3-7908-1073-8
ISBN 3-7908-0837-7
Vol. 16. D. Driankov and R. Palm (Eds.)
Vol. 5. P. Bose and J. Kacprzyk (Eds.) Advances in Fuzzy Control, i998
Fuzziness in Database Management ISBN 3-7908-1090-8
Systems, i995
Vol. 17. L. Reznik, V. Dimitrov
ISBN 3-7908-0858-X
and J. Kacprzyk (Eds.)
Vol. 6. E. S. Lee and Q. Zhu Fuzzy Systems Design, i998
Fuzzy and Evidence Reasoning, i995 ISBN 3-7908-1118-1
ISBN 3-7908-0880-6
Vol. 18. L. Polkowski and
Vol. 7. B.A. Juliano and W. Bandler A. Skowron (Eds.)
Tracing Chains-of-Thought, i996 Rough Sets in Knowledge
ISBN 3-7908-0922-5 Discovery i, i 998
ISBN 3-7908-1119-X
Vol. 8. F. Herrera and J. L. Verdegay (Eds.)
Genetic Algorithms and Soft Computing, i996
ISBN 3-7908-0956-X
Vol. 9. M. Sato eta!.
Fuzzy Clustering Models and Applications,
i997
ISBN 3-7908-1026-6
Vol. 10. L. C. Jain (Ed.)
Soft Computing Techniques in Knowledge
based intelligent Engineering Systems, i 997
ISBN 3-7908-1035-5
Vol. II. W. Mielczarski (Ed.)
Fuzzy Logic Techniques in Power Systems,
i998
ISBN 3-7908-1044-4
Vol. 12. B. Bouchon-Meunier (Ed.)
Aggregation and Fusion of Imperfect
Information, i998
ISBN 3-7908-1048-7
Vol. 13. E. Orlowska (Ed.)
incomplete Information: Rough Set Analysis,
i998
ISBN 3-7908-1049-5
Lech Polkowski · Andrzej Skowron (Eds.)
Rough Sets in
Knowledge Discovery 2
Applications, Case Studies and
Software Systems
With 88 Figures
and 131 Tables
Springer-Verlag Berlin Heidelberg GmbH
Prof. Dr. Sc. Lech Polkowski
Institute of Mathematics
Warsaw University of Technology
Pl. Politechniki 1
00-665 Warsaw, Poland
and
Polish-Japanese Institute of Computer Techniques
Koszykowa 86
02-008 Warsaw, Poland
Prof. Dr. Sc. Andrzej Skowron
Institute of Mathematics
Warsaw University
ul. Banacha 2
02-097 Warsaw, Poland
ISBN 978-3-7908-2459-9
Library of Congress Cataloging-in-Publication Data
Die Deutsche Bibliothek - CIP-Einheitsaufnahme
Rough sets in knowledge discovery I Lech Polkowski; Andrzej Skowron (eds.).
2. Applications, case studies and software systems: with 131 tables.
(Studies in fuzziness and soft computing; Vol. 19)
ISBN 978-3-7908-2459-9 ISBN 978-3-7908-1883-3 (eBook)
DOI 10.1007/978-3-7908-1883-3
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© Springer-Verlag Berlin Heidelberg 1998
Originally published by Physica-Verlag Heidelberg New York in 1998
The use of general descriptive names, registered names, trademarks, etc. in this publication does
not imply, even in the absence of a specific statement, that such names are exempt from the
relevant protective laws and regulations and therefore free for general use.
Hardcover Design: Erich Kirchner, Heidelberg
SPIN 10679055 8812202-5 4 3 2 1 0 - Printed on acid-free paper
Foreword
The papers on rough set theory and its applications placed in this volume present
a wide spectrum of problems representative to the present. stage of this theory.
Researchers from many countries reveal their rec.ent results on various aspects
of rough sets. The papers are not confined only to mathematical theory but
also include algorithmic aspects, applications and information about software
designed for data analysis based on this theory. The volume contains also list of
selected publications on rough sets which can be very useful to every one engaged
in research or applications in this domain and sometimes perhaps unaware of
results of other authors.
The book shows that rough set theory is a vivid and vigorous domain with
serious results to its credit and bright perspective for future developments. It lays
on the crossroads of fuzzy sets, theory of evidence, neural networks, Petri nets
and many other branches of AI, logic and mathematics. These diverse connec
tions seem to be a very fertile feature of rough set theory and have essentially
contributed to its wide and rapid expansion. It is worth mentioning that its
philosophical roots stretch down from Leibniz, Frege and Russell up to Popper.
Therefore many concepts dwelled on in rough set theory are not entirely new,
nevertheless the theory can be viewed as an independent discipline on its own
rights. Rough set theory has found many interesting real life applications in
medicine, banking, industry and others.
The rough set approach seems to be of fundamental importance to AI and
cognitive sciences, especially in the areas of machine learning, knowledge acqui
sition, decision analysis, knowledge discovery from databases, expert systems,
inductive reasoning and pattern recognition. It appears to be of particular impor
tance to decision support systems and data mining. Although rough set theory
has many achievements to its credit, nevertheless several theoretical and prac
tical problems require further attention. It is especially important to develop
widely accessible, efficient software for rough set based data analysis, partic
ularly for large collections of data. Despite of many valuable methods, based
on rough set theory, for efficient generation of optimal decision rules from data,
developed in recent years, more research is needed here, particularly, when quan
titative attributes are involved. In this context also new discretization methods
for quantitative attribute values are badly needed. Comparison with other sim
ilar methods still requires due attention, although important results have been
VI
obtained in this area. A study of the relationship between neural network and
rough set approaches tends to be particularly interesting. Image and signal pro
cessing using rough sets methods are felt to be also very promising areas. Re
cently rough data bases and rough information retrieval have been pursued by
many researchers. Last but not least, rough set computer is badly needed for
many advanced applications.
The volume not only provides many very interesting results but also, no
doubt, marks out future directions of developments of this domain.
Congratulations are due to Professors Lech Polkowski and Andrzej Skowron
for their marvelous job.
Zdzislaw Pawlak
Warsaw, February 1998
Contents
Foreword v
Z. Pawlak
L. Zadeh
Chapter 1. Introducing the Book 1
L. Polkowski and A. Skowron
PART 1. APPLICATIONS
Chapter 2. Rough Approximation of a Preference Relation
in a Pairwise Comparison Table 13
S. Greco, B. Matarazzo and R. Slowinski
Chapter 3. Learning Decision Rules from Similarity Based
Rough Approximations 37
K. Krawiec, R. Slowinski and D. Vanderpooten
Chapter 4. Discovery of Data Patterns with Applications
to Decomposition and Classification Problems 55
S. Hoa Nguyen, A. Skowron and P. Synak
Chapter 5. Answering Non-Standard Queries in Distributed
Knowledge-Based Systems 98
Z.W. Ras
Chapter 6. Approximation Spaces, Reducts and Representatives 109
J. Stepaniuk
Chapter 7. Data Mining: A Probabilistic Rough Set Approach 127
N. Zhong, J.Z. Dong and S. Ohsuga
VIII
PART 2: CASE STUDIES
Chapter 8. Soft Processing of Audio Signals 147
A. Czyzewski
Chapter 9. A Rough Set Approach to Information Retrieval 166
K. Funakoshi and T. Bao Ho
Chapter 10. Extraction Method Based on Rough Set Theory of Rule-Type
Knowledge from Diagnostic Cases of Slope-Failure Danger Levels 178
H. Furuta, M. Hirokane andY. Mikumo
Chapter 11. Soft Computing-Based Recognition of Musical Sounds 193
B. Kostek
Chapter 12. Rough Sets in Industrial Applications 214
A. Mrozek and L. Plonka
Chapter 13. Rough Sets in Economic Applications 238
A. Mrozek and K. Skabek
Chapter 14. Multistage Rough Set Analysis of Therapeutic Experience
with Acute Pancreatitis 272
K. Slowinski and J. Stefanowski
Chapter 15. Reduction Methods for Medical Data 295
H. Tanaka andY. Maeda
Chapter 16. Formalization and Induction of Medical Expert System
Rules Based on Rough Set Theory 307
S. Tsumoto
Chapter 17. Rough Sets for Database Marketing 324
D. Van den Poel
Chapter 18. A New Halftoning Method Based on Error Diffusion
with Rough Set Filtering 336
H. Zeng and R. Swiniarski
IX
PART 3: HYBRID APPROACHES
Chapter 19. IRIS Revisited: A Comparison of Discriminant and
Enhanced Rough Set Data Analysis 345
C. Browne, I. Diintsch and G. Gediga
Chapter 20. Applications of Rough Patterns 369
P. Lingras
Chapter 21. Time and Clock Information Systems: Concepts and
Roughly Fuzzy Petri Net Models 385
J .F. Peters III
Chapter 22. The Synthesis Problem of Concurrent Systems
Specified by Dynamic Information Systems 418
Z. Suraj
Chapter 23. Rough Sets and Artificial Neural Networks 449
M.S. Szczuka
Chapter 24. Genetic Algorithms in Decomposition and
Classification Problems 4 71
J. Wroblewski
APPENDIX 1: ROUGH SET BIBLIOGRAPHY
Selected Bibliography on Rough Sets 491
APPENDIX 2: SOFTWARE SYSTEMS
GROBIAN 555
I. Diintsch and G. Gediga
RSDM: Rough sets Data Miner, A System to Add
Data Mining Capabilities to RDBMS 558
M.C. Fernandez-Baizan, E. Menasalvas Ruiz,
J .M. Peiia and B. Pardo Pastrana
X
LERS- A Knowledge Discovery System 562
J. W. Grzymala-Busse
TRANCE: A Tool for Rough Data Analysis,
Classification, and Clu.stering 566
W. Kowalczyk
ProbRough - A System for Probabilistic Rough
Classifiers Generation 569
A. Lenarcik and Z. Piasta
The ROSETTA Software System 572
A. 0hrn, J. Komorowski, A. Skowron and P. Synak
RSL- The Rough Set Library 577
J. Sienkiewicz
Rough Family - Software Implementation of the Rough Set Theory 581
R. Slowinski and J. Stefanowski
TAS: Tools for Analysis and Synthesis of
Concurrent Processes Using Rough Set Methods 587
Z. Suraj
RoughF'tt.zzyLab - A System for Data Mining and Rough and F'tt.zzy
Sets Based Classification 591
R. W. Swiniarski
PRIMEROSE 594
S. Tsumoto
KDD-R: Rough Sets-Based Data Mining System 598
W. Ziarko