A Framework for Efficient Scalable Mining of Rule Variants

Association rule mining is an important data mining problem. Since its inception, different variants of rules has been proposed in the literature. In each case, different attributes (e.g., weight and quantity) are considered to obtain more informative rules. To our knowledge, each proposal is based on the Apriori algorithm that is, in modern context, inefficient. Methods that outperform the Apriori (e.g., FPGrowth and DiffSets) are restricted to the discovery of plain vanilla rules, and does not scale well to mining other variants. In this paper, we present an unifying framework for mining variants of rules that separates the scalability and performance aspects of Apriori-based algorithms from the constraints of mining each specific variant. This framework is easy to instantiate with algorithms proposed to date, and supports new algorithms considering future variants. More importantly, it favors the simplicity of the Apriori algorithm, leverages the performance to that of FP-Growth, and maintains a simple scalability model.