Foundations of Rough Sets from Vagueness Perspective

Foundations of Rough Sets from Vagueness Perspective

Piotr Wasilewski (Warsaw University, Poland) and Dominik Slezak (Infobright Inc., Canada)
Copyright: © 2008 |Pages: 37
DOI: 10.4018/978-1-59904-552-8.ch001
OnDemand PDF Download:


We present three types of knowledge, which can be specified according to the Rough Set theory. Then, we present three corresponding types of algebraic structures appearing in the Rough Set theory. This leads to three following types of vagueness: crispness, classical vagueness, and a new concept of “intermediate” vagueness. We also propose two classifications of information systems and approximation spaces. Based on them, we differentiate between information and knowledge.

Complete Chapter List

Search this Book:
Table of Contents
Chapter 1
Piotr Wasilewski, Dominik Slezak
We present three types of knowledge, which can be specified according to the Rough Set theory. Then, we present three corresponding types of... Sample PDF
Foundations of Rough Sets from Vagueness Perspective
Chapter 2
Hung Son Nguyen
This chapter presents the Boolean reasoning approach to problem solving and its applications in Rough sets. The Boolean reasoning approach has... Sample PDF
Rough Sets and Boolean Reasoning
Chapter 3
Richard Jensen
Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the... Sample PDF
Rough Set-Based Feature Selection: A Review
Chapter 4
Yiyu Yao
Rough set analysis (RSA) and formal concept analysis (FCA) are two theories of intelligent data analysis. They can be compared, combined and applied... Sample PDF
Rough Set Analysis and Formal Concept Analysis
Chapter 5
Theresa Beaubouef, Frederick E Petry
This chapter discusses ways in which rough set theory can enhance databases by allowing for the management of uncertainty. Rough sets can be... Sample PDF
Rough Sets: A Versatile Theory for Approaches to Uncertainty Management in Databases
Chapter 6
Cory J. Butz
In this chapter, we review a graphical framework for reasoning from data, called rough set flow graphs (RSFGs), and point out issues of current... Sample PDF
Current Trends in Rough Set Flow Graphs
Chapter 7
Annibal Parracho Sant’Anna
A new index of quality of approximation, called the index of mutual information, is proposed in this chapter. It measures the mutual information... Sample PDF
Probabilistic Indices of Quality of Approximation
Chapter 8
Zbigniew W. Ras, Elzbieta M. Wyrzykowska
Action rules can be seen as logical terms describing knowledge about possible actions associated with objects which is hidden in a decision system.... Sample PDF
Extended Action Rule Discovery Based on Single Classification Rules and Reducts
Chapter 9
James F Peters
This paper introduces a monocular vision system that learns with approximation spaces to control the pan and tilt operations of a digital camera... Sample PDF
Monocular Vision System that Learns with Approximation Spaces
Chapter 10
Tomasz G. Smolinski, Astrid A. Prinz
Classification of sampled continuous signals into one of a finite number of predefined classes is possible when some distance measure between the... Sample PDF
Hybridization of Rough Setsand Multi-ObjectiveEvolutionary Algorithms forClassificatory SignalDecomposition
Chapter 11
Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe, Teresa Mroczek
Results of our research on using two approaches, both based on rough sets, to mining three data sets describing bed caking during the hop extraction... Sample PDF
Two Rough Set Approaches to Mining Hop Extraction Data
Chapter 12
Krzysztof Pancerz, Zbigniew Suraj
This chapter constitutes the continuation of a new research trend binding rough set theory with concurrency theory. In general, this trend concerns... Sample PDF
Rough Sets for Discovering Concurrent System Models from Data Tables
About the Contributors