Ebook: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems

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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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.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Introducing the Book....Pages 3-7
A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book....Pages 9-45
Front Matter....Pages 47-47
Rough Set Algorithms in Classification Problem....Pages 49-88
Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning....Pages 89-135
Knowledge Discovery by Application of Rough Set Models....Pages 137-233
Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey....Pages 235-285
Front Matter....Pages 287-287
Regularity Analysis and its Applications in Data Mining....Pages 289-378
Rough Set Methods for the Synthesis and Analysis of Concurrent Processes....Pages 379-488
Front Matter....Pages 489-489
Conflict Analysis....Pages 491-519
Logical and Algebraic Techniques for Rough Set Data Analysis....Pages 521-544
Statistical Techniques for Rough Set Data Analysis....Pages 545-565
Data Mining in Incomplete Information Systems from Rough Set Perspective....Pages 567-580
Front Matter....Pages 581-581
Rough Sets and Rough Logic: A KDD Perspective....Pages 583-646
Back Matter....Pages 647-683

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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.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Introducing the Book....Pages 3-7
A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book....Pages 9-45
Front Matter....Pages 47-47
Rough Set Algorithms in Classification Problem....Pages 49-88
Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning....Pages 89-135
Knowledge Discovery by Application of Rough Set Models....Pages 137-233
Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey....Pages 235-285
Front Matter....Pages 287-287
Regularity Analysis and its Applications in Data Mining....Pages 289-378
Rough Set Methods for the Synthesis and Analysis of Concurrent Processes....Pages 379-488
Front Matter....Pages 489-489
Conflict Analysis....Pages 491-519
Logical and Algebraic Techniques for Rough Set Data Analysis....Pages 521-544
Statistical Techniques for Rough Set Data Analysis....Pages 545-565
Data Mining in Incomplete Information Systems from Rough Set Perspective....Pages 567-580
Front Matter....Pages 581-581
Rough Sets and Rough Logic: A KDD Perspective....Pages 583-646
Back Matter....Pages 647-683
....