
Rough Sets
Theoretical Aspects of Reasoning about Data
Z. Pawlak(Author)
Kluwer Academic Publishers
Published on 31. October 1991
Book
Hardback
XVI, 231 pages
978-0-7923-1472-1 (ISBN)
Description
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.
More details
Series
Edition
1991 ed.
Language
English
Place of publication
Dordrecht
Netherlands
Target group
Professional and scholarly
Research
Product notice
sewn/stitched
Cloth over boards
Illustrations
XVI, 231 p.
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 13 mm
Weight
481 gr
ISBN-13
978-0-7923-1472-1 (9780792314721)
DOI
10.1007/978-94-011-3534-4
Schweitzer Classification
Other editions
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Springer
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10/2012
Springer
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Content
I. Theoretical Foundations.- 1. Knowledge.- 2. Imprecise Categories, Approximations and Rough Sets.- 3. Reduction of Knowledge.- 4. Dependencies in Knowledge Base.- 5. Knowledge Representation.- 6. Decision Tables.- 7. Reasoning about Knowledge.- II. Applications.- 8. Decision Making.- 9. Data Analysis.- 10. Dissimilarity Analysis.- 11. Switching Circuits.- 12. Machine Learning.