Rough Sets [electronic resource] : Theoretical Aspects of Reasoning about Data /

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.

Bibliographic Details

Main Authors:	Pawlak, Zdzisław. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Dordrecht : Springer Netherlands : Imprint: Springer, 1991
Subjects:	Computer science., Operations research., Decision making., Artificial intelligence., Mathematical logic., Computer Science., Artificial Intelligence (incl. Robotics)., Mathematical Logic and Foundations., Operation Research/Decision Theory.,
Online Access:	http://dx.doi.org/10.1007/978-94-011-3534-4
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