This paper exhibits a Parameter-Free grammar guided genetic programming algorithm for mining rare association rules. This algorithm utilizes a context-free grammar to represent individuals, encoding the solutions in a tree-shape conformant to the grammar, so they are more expressive and flexible. The algorithm here introduced has the advantages of utilizing evolutionary algorithms for mining rare association rules, and it also additionally takes care of the issue of tuning the tremendous number of parameters required by these algorithms. The principle highlight of this algorithm is the small number of parameters required, providing the possibility of discovering rare association rules in an easy way for non-expert users. We compare our approach to existing evolutionary and exhaustive search algorithms, obtaining important results and overcoming the drawbacks of both exhaustive search and evolutionary algorithms. The experimental stage reveals that this approach discovers infrequent and reliable rules without a parameter tuning.

Genetic Programming, Association Rules, Free Parameters, Data Mining

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