Data Mining, Rough Sets and Granular Computing PDF
Preview Data Mining, Rough Sets and Granular Computing
Data 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; :
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