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      Information Fusion in Data Mining 

      Genetic Algorithms in Decomposition and Classification Problems

      other
      Physica-Verlag HD

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          Adaptation in Natural and Artificial Systems

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            The Discernibility Matrices and Functions in Information Systems

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              Rough Sets : Theoretical Aspects of Reasoning about Data

              Z. Pawlak (1991)
              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.
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                1998
                : 471-487
                10.1007/978-3-7908-1883-3_24
                ded867e8-48e1-4f43-aae4-949ff4226216
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