A rough set approach to mining concise rules from inconsistent data

In this paper, a rough set approach to mining concise rules from inconsistent data is proposed. The approach is based on the variable precision rough set model and deals with inconsistent data. By first computing the reduct for each concept, then computing the reduct for each object, this approach adopts a heuristic algorithm HCRI to build concise classification rules for each concept satisfying the given classification accuracy. HASH functions are designed for the implementation, which substantially reduce the computational complexity of the algorithm. UCI data sets are used to test the proposed approach. The results show that our approach effectively eliminates noises in data and greatly improves the total data reduction rate

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