A parallel genetic algorithm for rule mining

Rule mining consists of discovering valid and useful rules in large databases. As other data mining tasks, it is known to be time-consuming and I/O intensive. Evolutionary algorithms and parallelism are two important ways to deal with that performance problem. In this paper, we propose a parallel genetic algorithm for rule discovery, namely . We evaluated it on the Nursery School public domain data set available from the UCI Repository of Machine Learning databases. The results show that is efficient and allows to discover high quality rules.

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