An efficient algorithm for incremental mining of temporal association rules
Data & Knowledge Engineering
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
A highly parallel algorithm for frequent itemset mining
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
International Journal of Computational Science and Engineering
An FPGA-Based Accelerator for Frequent Itemset Mining
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate itemsets and a database into the hardware. Too many candidate itemsets and a large database would create a performance bottleneck. In this paper, we propose a HAsh-based and PiPelIned architecture (abbreviated as HAPPI) for hardware-enhanced association rule mining. We apply the pipeline methodology in the HAPPI architecture to compare itemsets with the database and collect useful information for reducing the number of candidate itemsets and items in the database simultaneously. When the database is fed into the hardware, candidate itemsets are compared with the items in the database to find frequent itemsets. At the same time, trimming information is collected from each transaction. Therefore, we can effectively reduce the frequency of loading the database into the hardware. As such, HAPPI solves the bottleneck problem in Apriori-based hardware schemes. We also derive some properties to investigate the performance of this hardware implementation. As shown by the experiment results, HAPPI significantly outperforms the previous hardware approach in terms of execution cycles.