A New Approach for Optimizing the Extraction of Association Rules

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.