A genetic programming free-parameter algorithm for mining association rules

This paper presents a free-parameter grammar-guided genetic programming algorithm for mining association rules. This algorithm uses a contex-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 presented has the advantages of using evolutionary algorithms for mining association rules, and it also solves the problem of tuning the huge number of parameters required by these algorithms. The main feature of this algorithm is the small number of parameters required, providing the possibility of discovering 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 frequent and reliable rules without a parameter tuning.