An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization

Association rules are one of the most frequently used tools for finding relationships between different attributes in a database. There are various techniques for obtaining these rules, the most common of which are those which give categorical association rules. However, when we need to relate attributes which are numeric and discrete, we turn to methods which generate quantitative association rules, a far less studied method than the above. In addition, when the database is extremely large, many of these tools cannot be used. In this paper, we present an evolutionary tool for finding association rules in databases (both small and large) comprising quantitative and categorical attributes without the need for an a priori discretization of the domain of the numeric attributes. Finally, we evaluate the tool using both real and synthetic databases.

► We present an evolutionary approach for finding quantitative association rules. ► It is not mandatory to carry out an apriori discretization. ► The fitness function includes the measures for finding the most significant rules. ► The algorithm is able to obtain overlapping rules and it can work under different levels of noise.