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Subgroup discovery is a prominent data mining method for discovering local patterns. Since often a set of very similar, overlapping subgroup patterns is retrieved, efficient methods for extracting a set of relevant subgroups are required. This paper presents a novel algorithm based on a vertical data structure, that not only discovers interesting subgroups quickly, but also integrates efficient filtering of patterns, that are considered irrelevant due to their overlap. Additionally, we show how the algorithm can be easily applied in a distributed setting. Finally, we provide an evaluation of the presented approach using representative data sets.