Parallel leap: large-scale maximal pattern mining in a distributed environment

When computationally feasible, mining extremely large databases produces tremendously large numbers of frequent patterns. In many cases, it is impractical to mine those datasets due to their sheer size; not only the extent of the existing patterns, but mainly the magnitude of the search space. Many approaches have been suggested such as sequential mining for maximal patterns or searching for all frequent patterns in parallel. So far, those approaches are still not genuinely effective to mine extremely large datasets. In this work we propose a method that combines both strategies efficiently, i.e. mining in parallel for the set of maximal patterns which, to the best of our knowledge, has never been proposed efficiently before. Using this approach we could mine significantly large datasets; with sizes never reported in the literature before. We are able to effectively discover frequent patterns in a database made of billion transactions using a 32 processors cluster in less than 2 hours