Fast Top-K association rule mining using rule generation property pruning

Traditional association rule mining algorithms can have a long runtime, high memory consumption, and generate a huge number of rules. Browsing through numerous rules and adjusting parameters to find just enough rules is a tedious task for users, who are often only interested in finding the strongest rules. Hence, many recent studies have focused on mining the top-k most frequent association rules that have a minimum confidence so as to limit the number of rules by ranking them by frequency. Though this redefined task has many applications, the performance of current algorithms remains an issue. To address this issue, this paper presents a novel algorithm named FTARM (Fast Top-K Association Rule Miner) to efficiently find the set of top-k association rules using a novel technique called Rule Generation Property Pruning (RGPP). This technique reduces the search space by analyzing the internal relationships between items of the database to be mined and the parameters set by users. Furthermore, a novel candidate pruning property is used by this technique to speed up the mining process. FTARM's efficiency was evaluated on various public benchmark datasets. A substantial reduction of the association rule mining time and memory usage was observed, and that FTARM has good scalability, which can benefit to many applications.