Efficient Top-k Frequent Itemset Mining on Massive Data

Abstract

Abstract Top- k frequent itemset mining (top- k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports. Rather than the subtle minimum support threshold specified in FIM, top- k FIM only needs the more understandable parameter of the result number. The existing algorithms require at least two passes of scan on the table, and incur high execution cost on massive data. This paper develops a prefix-partitioning-based PTF algorithm to mine top- k frequent itemsets efficiently, where each prefix-based partition keeps the transactions sharing the same prefix item. PTF can skip most of the partitions directly which cannot generate any top- k frequent itemsets. Vertical mining is developed to process the partitions of vertical representation with the high-support-first principle, and only a small fraction of the items are involved in the processing of the partitions. Two improvements are proposed to reduce execution cost further. Hybrid vertical storage mode maintains the prefix-based partitions adaptively and the candidate pruning reduces the number of the explored candidates. The extensive experimental results show that, on massive data, PTF can achieve up to 1348.53 times speedup ratio and involve up to 355.31 times less I/O cost compared with the state-of-the-art algorithms.