Horizontal Decomposition Of Data Table For Finding One Reduct

Attribute reduction, being one of the most essential tasks in rough set theory, is a challenge for data that does not fit in the available memory. This paper proposes new definitions of attribute reduction using horizontal data decomposition. Algorithms for computing superreduct and subsequently exact reducts of a data table are developed and experimentally verified. In the proposed approach, the size of subtables obtained during the decomposition can be arbitrarily small. Reducts of the subtables are computed independently from one another using any heuristic method for finding one reduct. Compared with standard attribute reduction methods, the proposed approach can produce superreducts that usually inconsiderably differ from an exact reduct. The approach needs comparable time and much less memory to reduce the attribute set. The method proposed for removing unnecessary attributes from superreducts executes relatively fast for bigger databases.