FPGA Acceleration for Intersection Computation in Frequent Itemset Mining

Shaobo Shi, Yue Qi, Qin Wang

Abstract: Frequent item set mining is an important researching area in data mining and Eclat is a typical and high performance frequent item set mining algorithm. However, the large numbers of sorted-set intersection computation in the algorithm limit the performance of the algorithm seriously. FPGA is a low-power and high-performance computing platform that has been applied to accelerate parallel data mining successfully. To deal with the problem of the large number intersection computation in Eclat, this paper proposed a FPGA solution to accelerate the intersection computation. And a full comparator matrix structure is provided to perform the parallel intersection computation. The experiment results show that our solution can achieve a speedup of 26.7x on intersection computation comparing to the best software implementation existed, and the full comparator matrix have a better scalability, thus the entire running time of the Eclat algorithm can be decreased extremely.