Article: Apriori-roaring: frequent pattern mining method based on compressed bitmaps Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2022 Vol.21 No.1 pp.48 - 65 Abstract: Association rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to execution time. However, as the size of datasets used in the analysis grows, memory usage tends to be the constraint instead, and this prevents these approaches from being used. This article presents a new method for data analysis called apriori-roaring. The apriori-roaring method is designed to identify frequent items with a focus on scalability. The implementation of this method employs compressed bitmap structures, which use less memory to store the original dataset and to calculate the support metric. The results show that apriori-roaring allows the identification of frequent elements with much lower memory usage and shorter execution time. In terms of scalability, our proposed approach outperforms the various traditional approaches available. Inderscience Publishers - linking academia, business and industry through research

Title: Apriori-roaring: frequent pattern mining method based on compressed bitmaps

Authors: Alexandre Colombo; Roberta Spolon; Aleardo Manacero Junior; Renata Spolon Lobato; Marcos Antônio Cavenaghi

Addresses: Computing Department, São Paulo State University, Bauru, SP, Brazil ' Computing Department, São Paulo State University, Bauru, SP, Brazil ' Department of Computer Science and Statistics, São Paulo State University, São José do Rio Preto, SP, Brazil ' Department of Computer Science and Statistics, São Paulo State University, São José do Rio Preto, SP, Brazil ' Faculty of Business, Humber Institute of Technology and Advanced Learning, Toronto, ON, Canada

Abstract: Association rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to execution time. However, as the size of datasets used in the analysis grows, memory usage tends to be the constraint instead, and this prevents these approaches from being used. This article presents a new method for data analysis called apriori-roaring. The apriori-roaring method is designed to identify frequent items with a focus on scalability. The implementation of this method employs compressed bitmap structures, which use less memory to store the original dataset and to calculate the support metric. The results show that apriori-roaring allows the identification of frequent elements with much lower memory usage and shorter execution time. In terms of scalability, our proposed approach outperforms the various traditional approaches available.

Keywords: frequent pattern mining; bitmap compression; data mining; association rules; knowledge discovery.

DOI: 10.1504/IJBIDM.2022.123805

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.1, pp.48 - 65

Received: 13 Jun 2020
Accepted: 22 Dec 2020
Published online: 04 Jul 2022 *

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Title: Apriori-roaring: frequent pattern mining method based on compressed bitmaps

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