Efficiently Mining Frequent Itemsets on Massive Data

Abstract

Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions. The existing algorithms cannot compute frequent itemsets on massive data efficiently, since they either require multiple-pass scans on the table or construct complex data structures which normally exceed the available memory on massive data. This paper proposes a novel precomputation-based frequent itemset mining (PFIM) algorithm to compute the frequent itemsets quickly on massive data. PFIM treats the transaction table as two parts: the large old table storing historical data and the relatively small new table storing newly generated data. PFIM first pre-constructs the quasi-frequent itemsets on the old table whose supports are above the lower-bound of the practical support level. Given the specified support threshold, PFIM can quickly return the required frequent itemsets on the table by utilizing the quasi-frequent itemsets. Three pruning rules are presented to reduce the size of the involved candidates. An incremental update strategy is devised to efficiently re-construct the quasi-frequent itemsets when the tables are merged. The extensive experimental results, conducted on synthetic and real-life data sets, show that PFIM has a significant advantage over the existing algorithms and runs two orders of magnitude faster than the latest algorithm.

Keywords

• Frequent itemset mining
• massive data
• PFIM algorithm
• pruning rule
• incremental update