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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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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In a couple of years that elapsed since the monograph edited by T. Y. Lin and N. Cercone: Rough Sets and Data Mining. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
discernibility characteristics, exact decision reduct, discernibility degree, similarity relation class, using rough set methods, generalized dynamic reducts, normalized decision functions, concurrent data models, generalized decision value, objective global conflict, reduct domain, rough set notions, decision reducts, global preference function, rough set data analysis, optimal similarity relation, rough mereological universe, rough set community, rough mereology, rough set approach, given decision table, generalized decision rules, discernibility measure, generalized decision function, particular decision classes Key Phrases - Capitalized Phrases (CAPs): (learn more)
Lecture Notes, New York, Kluwer Academic Publishers, International Conference, Computer Science Vol, Warsaw University of Technology, Morgan Kaufmann, Research Report, Polish Academy of Sciences, Information Sciences, Fundamenta Informaticae, International Symposium, San Mateo, John Wiley, Proceedings of the First, Knowledge-Based Systems, San Jose, Cambridge University Press, Menlo Park, San Diego, Fuzzy Systems, Proceedings of the Seventh, Studia Logica, World Congress, Academic Press New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
In a couple of years that elapsed since the monograph edited by T. Y. Lin and N. Cercone: Rough Sets and Data Mining. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
discernibility characteristics, exact decision reduct, discernibility degree, similarity relation class, using rough set methods, generalized dynamic reducts, normalized decision functions, concurrent data models, generalized decision value, objective global conflict, reduct domain, rough set notions, decision reducts, global preference function, rough set data analysis, optimal similarity relation, rough mereological universe, rough set community, rough mereology, rough set approach, given decision table, generalized decision rules, discernibility measure, generalized decision function, particular decision classes Key Phrases - Capitalized Phrases (CAPs): (learn more)
Lecture Notes, New York, Kluwer Academic Publishers, International Conference, Computer Science Vol, Warsaw University of Technology, Morgan Kaufmann, Research Report, Polish Academy of Sciences, Information Sciences, Fundamenta Informaticae, International Symposium, San Mateo, John Wiley, Proceedings of the First, Knowledge-Based Systems, San Jose, Cambridge University Press, Menlo Park, San Diego, Fuzzy Systems, Proceedings of the Seventh, Studia Logica, World Congress, Academic Press New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
7
books
cite this book:
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- Transactions on Rough Sets III (Lecture Notes in Computer Science / Transactions on Rough Sets) by James F. Peters on 5 pages
- Rough-Neuro-Computing by Sankar Kumar Pal on 4 pages
- Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, ... / Lecture Notes in Artificial Intelligence) by Dominik Slezak on page 459, and page 539
- Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 2, ... / Lecture Notes in Artificial Intelligence) by Dominik Slezak on page 381
- Transactions on Rough Sets IV (Lecture Notes in Computer Science / Transactions on Rough Sets) by James F. Peters on page 142
See all 7 books citing this book
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