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ABSTRACT
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.
INDEX TERMS
data mining, Association Rules, hardware-enhanced mining
CITATION
Ming-Syan Chen, Jen-Wei Huang, Ying-Hsiang Wen, "Hardware-Enhanced Association Rule Mining with Hashing and Pipelining", IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 784-795, June 2008, doi:10.1109/TKDE.2008.39
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