Rough Sets and Data Mining

Analysis of Imprecise Data

By: Nick Cercone, T. Y. Lin (Editor), N. Cercone (Editor)

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Hardcover | 30 November 1996

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452 Pages

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24.13 x 16.51 x 3.18

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Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases.
The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others.
Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

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Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Mathematics Mathematical Foundation Set Theory Databases Data Mining Artificial Intelligence

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Paperback

Published: 2nd October 2011

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Preface	p. 1
Rough Sets	p. 3
Data Mining: Trends in Research and Development	p. 9
A Review of Rough Set Models	p. 47
Rough Control: A Perspective	p. 77
Machine Learning and Knowledge Acquisition, Rough Sets, and the English Semantic Code	p. 91
Generation of Multiple Knowledge from Databases Based on Rough Set Theory	p. 109
Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing	p. 123
Rough Real Functions and Rough Controllers	p. 139
A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems	p. 149
Rough Sets as a Tool for Studying Attribute Dependencies in the Urinary Stones Treatment Data Set	p. 177
Data Mining Using Attribute-oriented Generalization and Information Reduction	p. 199
Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering	p. 229
Resolving Queries through Cooperation in Multi-Agent Systems	p. 239
Synthesis of Decision Systems from Data Tables	p. 259
Combination of Rough and Fuzzy Sets Based on Alpha-Level Sets	p. 301
Theories that Combine many Equivalence and Subset Relations	p. 323
Generalized Rough Sets in Contextual Spaces	p. 339
Maintenance of Reducts in the Variable Precision Rough Set Model	p. 355
Probabilistic Rough Classifiers with Mixture of Discrete and Continuous Attributes	p. 373
Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry	p. 385
Topological Rough Algebras	p. 411
Table of Contents provided by Blackwell. All Rights Reserved.

Rough Sets and Data Mining

Analysis of Imprecise Data

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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