Rough Sets and Data Mining: Analysis of Imprecise Data

✍ Scribed by T. Y. Lin, N. Cercone (auth.)

Publisher: Springer US
Year: 1996
Tongue: English
Leaves: 428
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xi
Front Matter....Pages 1-1
Rough Sets....Pages 3-7
Data Mining: Trends in Research and Development....Pages 9-45
A Review of Rough Set Models....Pages 47-75
Rough Control: A Perspective....Pages 77-88
Front Matter....Pages 89-89
Machine Learning & Knowledge Acquisition, Rough Sets, and the English Semantic Code....Pages 91-107
Generation of Multiple Knowledge from Databases Based on Rough Sets Theory....Pages 109-121
Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing....Pages 123-138
Rough Real Functions and Rough Controllers....Pages 139-147
A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems....Pages 149-175
Rough Sets as a Tool for Studying Attribute Dependencies in the Urinary Stones Treatment Data Set....Pages 177-196
Front Matter....Pages 197-197
Data Mining Using Attribute-Oriented Generalization and Information Reduction....Pages 199-227
Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering....Pages 229-238
Resolving Queries Through Cooperation in Multi-Agent Systems....Pages 239-258
Synthesis of Decision Systems from Data Tables....Pages 259-299
Combination of Rough and Fuzzy Sets Based on α-Level Sets....Pages 301-321
Theories that Combine Many Equivalence and Subset Relations....Pages 323-336
Front Matter....Pages 337-337
Generalized Rough Sets in Contextual Spaces....Pages 339-354
Maintenance of Reducts in the Variable Precision Rough Set Model....Pages 355-372
Probabilistic Rough Classifiers with Mixtures of Discrete and Continuous Attributes....Pages 373-383
Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry....Pages 385-410
Front Matter....Pages 337-337
Topological Rough Algebras....Pages 411-425
Back Matter....Pages 427-436

✦ Subjects

Artificial Intelligence (incl. Robotics); Data Structures, Cryptology and Information Theory; Mathematical Logic and Foundations

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