Attribute Reduction Method of Uncertain Information
Xu E, Liang Shan Shao, Qiao Zhu, Guang Hui Cao, Feng Qiu
Abstract
Xu E, Liang Shan Shao, Qiao Zhu, Guang Hui Cao, Feng Qiu
Abstract
To attribute reduction in an uncertain information system, this paper proposed a method of attribute reduction based on rough set theory. This reduction method gives the concepts of tolerance relationship, attribute significance and tolerance relationship similar matrix to deal with the inconsistency problem of in the information table. And then obtains the core attributes of incomplete information systems via the tolerance relationship similar matrix. Finally, according to attribute frequency in the tolerance relationship similar matrix, as the heuristic knowledge, makes use of binsearch heuristic algorithm to calculate the candidate attribute expansion so that it can reduce the expansion times to speed up reduction. Experiment results show that the algorithm is simple and effective.
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To attribute reduction in an uncertain information system, this paper proposed a method of attribute reduction based on rough set theory. This reduction method gives the concepts of tolerance relationship, attribute significance and tolerance relationship similar matrix to deal with the inconsistency problem of in the information table. And then obtains the core attributes of incomplete information systems via the tolerance relationship similar matrix. Finally, according to attribute frequency in the tolerance relationship similar matrix, as the heuristic knowledge, makes use of binsearch heuristic algorithm to calculate the candidate attribute expansion so that it can reduce the expansion times to speed up reduction. Experiment results show that the algorithm is simple and effective.
Key concepts: Rough set, Reduction (mathematics), Heuristic, Matrix (chemical analysis), Attribute domain, Set (abstract data type), Algorithm, Data mining