Attribute reduction: a horizontal data decomposition approach

Ever-growing data generate a need for new solutions to the problem of attribute reduction. Such solutions are required to deal with limited memory capacity and with many computations needed for large data processing. This paper proposes new definitions of attribute reduction using horizontal data decomposition. Algorithms for computing reducts of an information system and decision table are developed and evaluated. In the proposed approach, the size of subtables obtained during the decomposition can be arbitrarily small. The reduct sets of subtables are computed independently from one another using any heuristic method for attribute reduction. Compared with standard attribute reduction methods, the proposed approach can produce the same reducts with less space complexity and with the same or less theoretical time complexity. Experiments conducted under this work show that for information systems with fewer attributes or reducts the time needed for computing the reduct set can be shorten.