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Volume 13 Number 3 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/754

Title: The Development of the Generalization Algorithm Based on the Rough Set Theory
Authors: Fomina, Marina
Kulikov, Alexey
Vagin, Vadim
Keywords: Knowledge Acquisition
Knowledge Discovery
Generalization Problem
Rough Sets
Discretization Algorithm
Issue Date: 2006
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: This paper considers the problem of concept generalization in decision-making systems where such features of real-world databases as large size, incompleteness and inconsistence of the stored information are taken into account. The methods of the rough set theory (like lower and upper approximations, positive regions and reducts) are used for the solving of this problem. The new discretization algorithm of the continuous attributes is proposed. It essentially increases an overall performance of generalization algorithms and can be applied to processing of real value attributes in large data tables. Also the search algorithm of the significant attributes combined with a stage of discretization is developed. It allows avoiding splitting of continuous domains of insignificant attributes into intervals.
URI: http://hdl.handle.net/10525/754
ISSN: 1313-0463
Appears in Collections:Volume 13 Number 3

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