Abstract
ABSTRACT Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent minimum-support and exponential search space. Database-dependent minimum-support means that users must specify suitable thresholds for their mining tasks though they may have no knowledge concerning their databases. To circumvent these problems, in this paper, we design an evolutionary mining strategy, namely the ARMGA model, based on a genetic algorithm. Like general genetic algorithms, our ARMGA model is effective for global searching, especially when the search space is so large that it is hardly possible to use deterministic searching method.
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