Rough Sets: Theoretical Aspects of Reasoning about Data

✍ Scribed by Zdzisław Pawlak (auth.)

• Publisher: Springer Netherlands
• Year: 1991
• Tongue: English
• Leaves: 246
• Series: Theory and Decision Library 9
• Edition: 1
• Category: Library

✦ Synopsis

To-date computers are supposed to store and exploit knowledge. At least that is one of the aims of research fields such as Artificial Intelligence and Information Systems. However, the problem is to understand what knowledge means, to find ways of representing knowledge, and to specify automated machineries that can extract useful information from stored knowledge. Knowledge is something people have in their mind, and which they can express through natural language. Knowl­ edge is acquired not only from books, but also from observations made during experiments; in other words, from data. Changing data into knowledge is not a straightforward task. A set of data is generally disorganized, contains useless details, although it can be incomplete. Knowledge is just the opposite: organized (e.g. laying bare dependencies, or classifications), but expressed by means of a poorer language, i.e. pervaded by imprecision or even vagueness, and assuming a level of granularity. One may say that knowledge is summarized and organized data - at least the kind of knowledge that computers can store.

✦ Table of Contents

Front Matter....Pages i-xvi
Knowledge....Pages 1-8
Imprecise Categories, Approximations and Rough Sets....Pages 9-32
Reduction of Knowledge....Pages 33-44
Dependencies in Knowledge Base....Pages 45-50
Knowledge Representation....Pages 51-67
Decision Tables....Pages 68-80
Reasoning about Knowledge....Pages 81-115
Decision Making....Pages 116-132
Data Analysis....Pages 133-163
Dissimilarity Analysis....Pages 164-187
Switching Circuits....Pages 188-204
Machine Learning....Pages 205-224
Back Matter....Pages 225-231

✦ Subjects

Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Operation Research/Decision Theory