A Rule Acquisition Method Based on Rough Set Theory and Genetic Algorithm

It is an objective fact that large database has inconsistent data. This paper presents a new rule acquisition method based on rough set theory and genetic algorithm. Using rough set theory, we will divide inconsistent data table into two parts, certain data and possible data, and then standard genetic algorithm is used for mining rules set. When the algorithm is processing, the user is allowed to set three evaluation parameter values of the rules: support, confidence, coverage for specific application needs. This algorithm will delete the rules which do not meet the requirements, so we can reduce the amount of data in the case of massive data. The advantage of this setting is obvious. Finally, we use an case to verify this method.