Rough Sets : Theoretical Aspects of Reasoning about Data

Z. Pawlak

ISBN 10: 0792314727 / ISBN 13: 9780792314721

Published by Springer Okt 1991, 1991

About this Item

Neuware - 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.

Seller Inventory # 9780792314721

Bibliographic Details

• Title: Rough Sets : Theoretical Aspects of Reasoning about Data
• Publisher: Springer Okt 1991
• Publication Date: 1991
• Language: English
• ISBN 10: 0792314727
• ISBN 13: 9780792314721
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 156H x 13D millimeters 234 millimeters width by 156 millimeters height by 13 millimeters depth
• Weight: 481 grams

About this title

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.