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Rough Sets and Data Mining Analysis of Imprecise Data

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, unc...

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Bibliographic Details
Other Authors: Lin, T.Y. (Editor), Cercone, N. (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 1997, 1997
Edition:1st ed. 1997
Subjects:
Information Theory
Mathematical Logic
Data Structures And Information Theory
Data Structures (computer Science)
Artificial Intelligence
Mathematical Logic And Foundations
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • I Expositions
  • 1 Rough Sets
  • 2 Data Mining: Trends in Research and Development
  • 3 A Review of Rough Set Models
  • 4 Rough Control: A Perspective
  • II Applications
  • 5 Machine Learning & Knowledge Acquisition, Rough Sets, and The English Semantic Code
  • 6 Generation of Multiple Knowledge From Databases Basedon Rough Set Theory
  • 7 Fuzzy Controllers:An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing
  • 8 Rough Real Functions and Rough Controllers
  • 9 A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms For Hybrid Diagnostic Systems
  • 10 Rough Sets As A Tool For Studying Attribute Dependencies in The Urinary Stones Treatment Data Set
  • III Related Areas
  • 11 Data Mining Using Attribute- Oriented Generalization and Information Reduction
  • 12 Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering
  • 13 Resolving Queries Through Cooperation in Multi-Agent Systems
  • 14 Synthesis of Decision Systems From Data Tables
  • 15 Combination Of Rough and Fuzzy Sets Based on Alpha-Level Sets
  • 16 Theories That Combine Many Equivalence and Subset Relations
  • IV Generalization
  • 17 Generalized Rough Sets in Contextual Spaces
  • 18 Maintenance Of Reducts in The Variable Precision Rough Set Model
  • 19 Probabilistic Rough Classifiers With Mixture Of Discrete and Continuous Attributes
  • 20 Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry
  • 21 Topological Rough Algebras

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