FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review

Bustio-Martinez L, Cumplido R, Letras M, Hernandez-Leon R, Feregrino-Uribe C, Hernandez-Palancar J (2022)

Publication Type: Journal article, Review article

Publication year: 2022


Book Volume: 54

Journal Issue: 9

DOI: 10.1145/3472289


In data mining, Frequent Itemsets Mining is a technique used in several domains with notable results. However, the large volume of data in modern datasets increases the processing time of Frequent Itemset Mining algorithms, making them unsuitable for many real-world applications. Accordingly, proposing new methods for Frequent Itemset Mining to obtain frequent itemsets in a realistic amount of time is still an open problem. A successful alternative is to employ hardware acceleration using Graphics Processing Units (GPU) and Field Programmable Gates Arrays (FPGA). In this article, a comprehensive review of the state of the art of Frequent Itemsets Mining hardware acceleration is presented. Several approaches (FPGA and GPU based) were contrasted to show their weaknesses and strengths. This survey gathers the most relevant and the latest research efforts for improving the performance of Frequent Itemsets Mining regarding algorithms advances and modern development platforms. Furthermore, this survey organizes the current research on Frequent Itemsets Mining from the hardware perspective considering the source of the data, the development platform, and the baseline algorithm.

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Bustio-Martinez, L., Cumplido, R., Letras, M., Hernandez-Leon, R., Feregrino-Uribe, C., & Hernandez-Palancar, J. (2022). FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review. ACM Computing Surveys, 54(9). https://dx.doi.org/10.1145/3472289


Bustio-Martinez, Lazaro, et al. "FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review." ACM Computing Surveys 54.9 (2022).

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