survey

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

Authors:

Lázaro Bustio-Martínez,

René Cumplido,

Martín Letras,

Raudel Hernández-León,

Claudia Feregrino-Uribe,

José Hernández-PalancarAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 9

Article No.: 179, Pages 1 - 35

https://doi.org/10.1145/3472289

Published: 08 October 2021 Publication History

Abstract

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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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 9

December 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3485140

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 08 October 2021

Accepted: 01 June 2021

Revised: 01 February 2021

Received: 01 June 2020

Published in CSUR Volume 54, Issue 9

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https://dl.acm.org/doi/10.1155/2024/4420578
Chon KKim C(2024)GMiner++: Boosting GPU-based frequent itemset mining by reducing redundant computationsExpert Systems with Applications10.1016/j.eswa.2024.123928250(123928)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123928
Olson D(2024)Data MiningEncyclopedia of Optimization10.1007/978-3-030-54621-2_108-1(1-7)Online publication date: 11-Aug-2024
https://doi.org/10.1007/978-3-030-54621-2_108-1
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Li BZhang XBan YXu XSu WChen JZhang SLi FLiang ZZhou S(2022)Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning AlgorithmScientific Programming10.1155/2022/96077552022Online publication date: 1-Jan-2022
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Zoraghchian ASohrabi MYaghmaee F(2022)Parallel frequent itemsets mining using distributed graphic processing unitsMultimedia Tools and Applications10.1007/s11042-022-13225-z81:30(43873-43895)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-13225-z

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