Scrutinizing frequent pattern discovery performance

Abstract

Benchmarking technical solutions is as important as the solutions themselves. Yet many fields still lack any type of rigorous evaluation. Performance benchmarking has always been an important issue in databases and has played a significant role in the development, deployment and adoption of technologies. To help assessing the myriad algorithms for frequent itemset mining, we built an open framework and testbed to analytically study the performance of different algorithms and their implementations, and contrast their achievements given different data characteristics, different conditions, and different types of patterns to discover and their constraints. This facilitates reporting consistent and reproducible performance results using known conditions.