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Rough set methods and applications: new developments in knowledge discovery in information systems

Contributor(s): Publication details: Physica-Verlag 2000 New YorkDescription: x, 681 pISBN:
  • 9783662003763
Subject(s): DDC classification:
  • 006.3 R6
Summary: 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. (http://www.springer.com/us/book/9783662003763)
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 3-B / Slot 105 (0 Floor, West Wing) Non-fiction General Stacks 006.3 R6 (Browse shelf(Opens below)) Available 191461

Table of Contents:


Chapter-I: A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book

Chapter-II: A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on Rough Set Algorithms in Classification Problem

Chapter-III: Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning

Chapter-IV: Knowledge Discovery by Application of Rough Set Models

Chapter-V: Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey

Chapter-VI: Regularity Analysis and its Applications in Data Mining

Chapter-VII: Rough Set Methods for the Synthesis and Analysis of Concurrent Processes

Chapter-VIII: Conflict Analysis

Chapter-IX: Logical and Algebraic Techniques for Rough Set Data Analysis

Chapter-X: Statistical Techniques for Rough Set Data Analysis

Chapter-XI: Data Mining in Incomplete Information Systems from Rough Set Perspective

Chapter-XII: Rough Sets and Rough Logic: A KDD Perspective




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.


(http://www.springer.com/us/book/9783662003763)

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