Rough Sets and Data Mining
Analysis of Imprecise Data
Published on 30. November 1996
Book
Hardback
XII, 436 pages
978-0-7923-9807-3 (ISBN)
Description
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.
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.
More details
Edition
1997 ed.
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Research
Illustrations
XII, 436 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 29 mm
Weight
840 gr
ISBN-13
978-0-7923-9807-3 (9780792398073)
DOI
10.1007/978-1-4613-1461-5
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2012
Springer
€149.79
Available for download
Book
10/2011
Springer
€160.49
Shipment within 15-20 days
Content
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.