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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基于 FPGA/GPU 的频繁项集挖掘加速：综合回顾

在数据挖掘中，频繁项集挖掘是一种在多个领域中使用并取得显著成果的技术。然而，现代数据集中的大量数据增加了频繁项集挖掘算法的处理时间，使其不适用于许多实际应用。因此，提出新的频繁项集挖掘方法以在实际时间内获得频繁项集仍然是一个悬而未决的问题。一个成功的替代方案是使用图形处理单元 (GPU) 和现场可编程门阵列 (FPGA) 来使用硬件加速。在本文中，全面回顾了频繁项集挖掘硬件加速的最新技术。对比了几种方法（基于 FPGA 和 GPU）以显示它们的弱点和优势。该调查收集了最相关和最新的研究成果，以提高频繁项集挖掘在算法进步和现代开发平台方面的性能。此外，本次调查从硬件角度组织了当前对频繁项集挖掘的研究，考虑了数据的来源、开发平台和基线算法。