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140122 ||| eng |
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|a 9781461314615
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|a Lin, T.Y.
|e [editor]
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| 245 |
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|a Rough Sets and Data Mining
|h Elektronische Ressource
|b Analysis of Imprecise Data
|c edited by T.Y. Lin, N. Cercone
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| 250 |
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|a 1st ed. 1997
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| 260 |
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|a New York, NY
|b Springer US
|c 1997, 1997
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| 300 |
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|a XII, 436 p
|b online resource
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| 505 |
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|a I Expositions -- 1 Rough Sets -- 2 Data Mining: Trends in Research and Development -- 3 A Review of Rough Set Models -- 4 Rough Control: A Perspective -- II Applications -- 5 Machine Learning & Knowledge Acquisition, Rough Sets, and The English Semantic Code -- 6 Generation of Multiple Knowledge From Databases Basedon Rough Set Theory -- 7 Fuzzy Controllers:An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing -- 8 Rough Real Functions and Rough Controllers -- 9 A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms For Hybrid Diagnostic Systems -- 10 Rough Sets As A Tool For Studying Attribute Dependencies in The Urinary Stones Treatment Data Set -- III Related Areas -- 11 Data Mining Using Attribute- Oriented Generalization and Information Reduction -- 12 Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering -- 13 Resolving Queries Through Cooperation in Multi-Agent Systems -- 14 Synthesis of Decision Systems From Data Tables -- 15 Combination Of Rough and Fuzzy Sets Based on Alpha-Level Sets -- 16 Theories That Combine Many Equivalence and Subset Relations -- IV Generalization -- 17 Generalized Rough Sets in Contextual Spaces -- 18 Maintenance Of Reducts in The Variable Precision Rough Set Model -- 19 Probabilistic Rough Classifiers With Mixture Of Discrete and Continuous Attributes -- 20 Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry -- 21 Topological Rough Algebras
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| 653 |
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|a Information theory
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| 653 |
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|a Mathematical logic
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| 653 |
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|a Data Structures and Information Theory
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| 653 |
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|a Data structures (Computer science)
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Mathematical Logic and Foundations
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| 653 |
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|a Artificial Intelligence
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| 700 |
1 |
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|a Cercone, N.
|e [editor]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 028 |
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|a 10.1007/978-1-4613-1461-5
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| 856 |
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|u https://doi.org/10.1007/978-1-4613-1461-5?nosfx=y
|x Verlag
|3 Volltext
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|a 006.3
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| 520 |
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|a 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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