Data Mining, Rough Sets and Granular Computing

By: Tsau Young Lin (Editor), Yiyu Y. Yao (Editor), Lotfi A. Zadeh (Editor)

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Hardcover | 10 April 2002

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552 Pages

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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.

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Preface	p. V
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	p. 3
Granular Computing in Data Mining
Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules	p. 23
Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems	p. 46
Validation of Concept Representation with Rule Induction and Linguistic Variables	p. 91
Granular Computing Using Information Tables	p. 102
A Query-Driven Interesting Rule Discovery Using Association and Spanning Operations	p. 125
Data Mining
An Interactive Visualization System for Mining Association Rules	p. 145
Algorithms for Mining System Audit Data	p. 166
Scoring and Ranking the Data Using Association Rules	p. 190
Finding Unexpected Patterns in Data	p. 216
Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model	p. 232
Granular Computing
Observability and the Case of Probability	p. 249
Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices	p. 265
Granular Computing with Closeness and Negligibility Relations	p. 290
Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty	p. 308
Basic Issues of Computing with Granular Probabilities	p. 339
Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle	p. 350
On Optimal Fuzzy Information Granulation	p. 364
Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales	p. 398
A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space	p. 414
Rough Sets and Granular Computing
GRS: A Generalized Rough Sets Model	p. 447
Structure of Upper and Lower Approximation Spaces of Infinite Sets	p. 461
Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures	p. 474
Granularity, Multi-valued Logic, Bayes' Theorem and Rough Sets	p. 487
The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model	p. 499
Possibilistic Data Analysis and Its Similarity to Rough Sets	p. 518
Table of Contents provided by Publisher. All Rights Reserved.

Data Mining, Rough Sets and Granular Computing

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Hardcover

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