Association rule mining using a multi-objective grammar-based ant programming algorithm

This paper presents a method for extracting association rules by means of a multi-objective grammar guided ant programming algorithm. Solution construction is guided by a context-free grammar specifically suited for association rule mining, which defines the search space of all possible expressions or programs. Evaluation of individuals is considered from a Pareto-based point of view, measuring support and confidence of rules mined, and assigning them a ranking fitness. The proposed algorithm is verified over 10 varied data sets and compared to other association rule mining algorithms from several paradigms such as exhaustive search, genetic algorithms and genetic programming, showing that ant programming is a good technique at addressing the association task of data mining as well.