Table Of ContentData Mining, Rough Sets
and Granular Computing
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:
Tsau Young Lin
Yiyu Y. Yao . Lotfi A. Zadeh
Editors
Data Mining,
Rough Sets
and Granular Computing
With 104 Figures
and 56 Tables
Springer-Verlag Berlin Heidelberg GmbH
Professor Tsau Young Lin
San lose State University
The Metropolitan University of Silicon Valley
Department of Mathematics and Computer Science
One Washington Square
San lose, CA 95192-0103
USA
Preface
During the past few years, data mining has grown rapidly in visibility and
importance within information processing and decision analysis. This is par-
ticularly true in the realm of e-commerce, where data mining is moving from
a "nice-to-have" to a "must-have" status.
In a different though related context, a new computing methodology called
granular computing is emerging as a powerful tool for the conception, analysis
and design of information/intelligent systems. In essence, data mining deals
with summarization of information which is resident in large data sets, while
granular computing plays a key role in the summarization process by draw-
ing together points (objects) which are related through similarity, proximity
or functionality. In this perspective, granular computing has a position of
centrality in data mining.
Another methodology which has high relevance to data mining and plays
a central role in this volume is that of rough set theory. Basically, rough
set theory may be viewed as a branch of granular computing. However, its
applications to data mining have predated that of granular computing.
This volume is the result of a two-year project aimed at coalescing the
concepts and techniques of granular computing on one side, and rough set
theory on another. It consists of a collection of up-to-date and authoritative
expositions of the basic theories underlying data mining, granular computing
and rough set theory, and stresses their wide-ranging applications. A principal
aim of our work is to stimulate an exploration of ways in which progress in
data mining can be enhanced through integration with granular computing
and rough set theory.
T.Y. Lin, Y.Y. Yao, L.A. Zadeh
Contents
Preface v
T.Y. Lin, Y.Y. Yao and L.A. Zadeh
PART 1: GRANULAR COMPUTING - A NEW PARADIGM
Some Reflections on Information Granulation and its Centrality
in Granular Computing, Computing with Words, the Computational
Theory of Perceptions and Precisiated Natural Language 3
L.A. Zadeh
PART 2: GRANULAR COMPUTING IN DATA MINING
Data Mining Using Granular Computing: Fast Algorithms for Finding
Association Rules 23
T.Y. Lin and E. Louie
Knowledge Discovery with Words Using Cartesian Granule Features:
An Analysis for Classification Problems 46
J.G. Shanahan
Validation of Concept Representation with Rule Induction and
Linguistic Variables 91
S. Tsumoto
Granular Computing Using Information Tables 102
Y.Y. Yao and N. Zhong
A Query-Driven Interesting Rule Discovery Using
Association and Spanning Operations 125
J.P. Yoon and L. Kerschberg
VIII
PART 3: DATA MINING
An Interactive Visualization System for Mining
Association Rules 145
J. Han, N. Cercone and X. Hu
Algorithms for Mining System Audit Data 166
W. Lee, S.J. Stolfo and K.W. Mok
Scoring and Ranking the Data Using Association Rules 190
B. Liu, Y. Ma and C.K. Wong
Finding Unexpected Patterns in Data 216
B. Padmanabhan and A. Tuzhilin
Discovery of Approximate Knowledge in Medical Databases
Based on Rough Set Model 232
S. Tsumoto
PART 4: GRANULAR COMPUTING
Observability and the Case of Probability 249
C. Alsina, J. Jacas and E. Trillas
Granulation and Granularity via Conceptual Structures:
A Perspective Prom the Point of View of Fuzzy Concept Lattices 265
R. Belohlavek
Granular Computing with Closeness and Negligibility Relations 290
D. Dubois, A. Hadj-Ali and H. Prade
Application of Granularity Computing to Confirm Compliance with
Non-Proliferation Treaty 308
A. Fattah, V. Pouchkarev, A.Belenki,
A.Ryjov and L.A. Zadeh
IX
Basic Issues of Computing with Granular Probabilities 339
G.J. Klir
Multi-dimensional Aggregation of Fuzzy Numbers
Through the Extension Principle 350
G. Mayor, A.R. de Soto, J. Suiier and E. Trillas
On Optimal Fuzzy Information Granulation 364
A. Ryjov
Ordinal Decision Making with a Notion of Acceptable:
Denoted Ordinal Scales 398
R.R. Yager
A Framework for Building Intelligent Information-Processing Systems
Based on Granular Factor Space 414
F. Yu and C. Huang
PART 5: ROUGH SETS AND GRANULAR COMPUTING
GRS: A Generalized Rough Sets Model 447
X. Hu, N. Cercone, J. Han and W. Ziarko
Structure of Upper and Lower Approximation Spaces of Infinite Sets 461
D.S. Malik and J.N. Mordeson
Indexed Rough Approximations, A Polymodal System, and
Generalized Possibility Measures 474
S. Miyamoto
Granularity, Multi-valued Logic, Bayes' Theorem and Rough Sets 487
Z. Pawlak
The Generic Rough Set Inductive Logic Programming (gRS-ILP)
Model 499
A. Siromoney and K. Inoue
Possibilistic Data Analysis and Its Similarity to Rough Sets 518
H. Tanaka and P. Guo
Part 1
Granular Computing -
A New Paradigm
Some Reflections on Information Granulation and its
Centrality in Granular Computing, Computing with
Words, the Computational Theory of Perceptions and
Precisiated Natural Language
Lotfi A. Zadeh
Berkeley Initiative in Soft Computing (BISC), Computer Science Division and the
Electronics Research Laboratory, Department of EECS, University of California,
Berkeley, CA 94720-1776; :
