Effective algorithm for mining compressed frequent patterns

Researches of frequent-pattern mining have recently focused on discovering representative patterns to compress a large of results within a reasonable tolerance bound. A novel heuristic algorithm, approximating mining based simulated annealing (AMSA), was proposed. The algorithm uses a method based simulated-annealing to improve efficiency and quality of the compression. Our experimental studies demonstrate the algorithm is efficient and high quality on a common dataset supported by frequent itemset mining implementations repository (FIMI). The mining result of AMSA is smaller than mining results of FPclose and RPglobal by performance study. Especially, if min_sup threshold is low, RPglobal fails to generate any result within reasonable time range, while AMSA generates a concise and succinct mining result.