Advances in Frequent Itemset Mining Implementations: Report on FIMI'03

Since the introduction of association rule mining in 1993 by Agrawal Imielinski and Swami, the frequent itemset mining (FIM) tasks have received a great deal of attention. Within the last decade, a phenomenal number of algorithms have been developed for mining all, closed and maximal frequent itemsets. Every new paper claims to run faster than previously existing algorithms, based on their experimental testing, which is oftentimes quite limited in scope, since many of the original algorithms are not available due to intellectual property and copyright issues. Zheng, Kohavi and Mason observed that the performance of several of these algorithms is not always as claimed by its authors, when tested on some different datasets. Also, from personal experience, we noticed that even different implementations of the same algorithm could behave quite differently for various datasets and parameters. Given this proliferation of FIM algorithms, and sometimes contradictory claims, there is a pressing need to benchmark, characterize and understand the algorithmic performance space. We would like to understand why and under what conditions one algorithm outperforms another. This means testing the methods for a wide variety of parameters, and on different datasets spanning dense and sparse, real and synthetic, small and large, and so on