Rough Set Methods and Applications
New Developments in Knowledge Discovery in Information Systems
Published on 8. October 2012
Book
Paperback/Softback
X, 683 pages
978-3-662-00376-3 (ISBN)
Description
Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.
More details
Series
Edition
Softcover reprint of the original 1st ed. 2000
Language
English
Place of publication
Heidelberg
Germany
Target group
Professional and scholarly
Research
Illustrations
X, 683 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 38 mm
Weight
1036 gr
ISBN-13
978-3-662-00376-3 (9783662003763)
DOI
10.1007/978-3-7908-1840-6
Schweitzer Classification
Other editions
Additional editions
Lech Polkowski | Shusaku Tsumoto | Tsau Y. Lin
Rough Set Methods and Applications
New Developments in Knowledge Discovery in Information Systems
Book
11/2000
Physica
€85.59
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Content
1. Introduction.- Introducing the Book.- 1. A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book.- 2. Methods and Applications: Reducts, Similarity, Mereology.- 2. Rough Set Algorithms in Classification Problem.- 3. Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning.- 4. Knowledge Discovery by Application of Rough Set Models.- 5. Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey.- 3. Methods and Applications: Regular Pattern Extraction, Concurrency.- 6. Regularity Analysis and its Applications in Data Mining.- 7. Rough Set Methods for the Synthesis and Analysis of Concurrent Processes.- 4. Methods and Applications: Algebraic and Statistical Aspects, Conflicts, Incompleteness.- 8. Conflict Analysis.- 9. Logical and Algebraic Techniques for Rough Set Data Analysis.- 10. Statistical Techniques for Rough Set Data Analysis.- 11. Data Mining in Incomplete Information Systems from Rough Set Perspective.- 5. Afterword.- 12. Rough Sets and Rough Logic: A KDD Perspective.- Appendix: Selected Bibliofgraphy on Rough Sets.