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" Rough Set Methods and Applications : "
edited by Lech Polkowski, Shusaku Tsumoto, Tsau Y. Lin.
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BL
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| Record Number
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768260
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b588246
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| Main Entry
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edited by Lech Polkowski, Shusaku Tsumoto, Tsau Y. Lin.
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| Title & Author
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Rough Set Methods and Applications : : New Developments in Knowledge Discovery in Information Systems\ edited by Lech Polkowski, Shusaku Tsumoto, Tsau Y. Lin.
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| Publication Statement
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Heidelberg : Physica-Verlag HD, 2000
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| Series Statement
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Studies in Fuzziness and Soft Computing, 56.
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| Page. NO
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1 ressource en ligne (x, 683 pages)
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| ISBN
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3790818402
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: 9783790818406
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| Contents
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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.
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| Abstract
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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.
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| Subject
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Business -- Data processing.
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Computer science.
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Information technology.
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| LC Classification
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QA76.9.D343E358 2000
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| Added Entry
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Lech Polkowski
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Shusaku Tsumoto
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Tsau Y Lin
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