article

On the design of hardware-software architectures for frequent itemsets mining on data streams

Authors:

Lázaro Bustio-Martínez,

René Cumplido,

Raudel Hernández-León,

José M. Bande-Serrano,

Claudia Feregrino-UribeAuthors Info & Claims

Journal of Intelligent Information Systems, Volume 50, Issue 3

Pages 415 - 440

Published: 01 June 2018 Publication History

Abstract

Frequent Itemsets Mining has been applied in many data processing applications with remarkable results. Recently, data streams processing is gaining a lot of attention due to its practical applications. Data in data streams are transmitted at high rates and cannot be stored for offline processing making impractical to use traditional data mining approaches (such as Frequent Itemsets Mining) straightforwardly on data streams. In this paper, two single-pass parallel algorithms based on a tree data structure for Frequent Itemsets Mining on data streams are proposed. The presented algorithms employ Landmark and Sliding Window Models for windows handling. In the presented paper, as in other revised papers, if the number of frequent items on data streams is low then the proposed algorithms perform an exact mining process. On the contrary, if the number of frequent patterns is large the mining process is approximate with no false positives produced. Experiments conducted demonstrate that the presented algorithms outperform the processing time of the hardware architectures reported in the state-of-the-art.

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Cited By

Bustio-Martínez LCumplido RLetras MHernández-León RFeregrino-Uribe CHernández-Palancar J(2021)FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive ReviewACM Computing Surveys10.1145/347228954:9(1-35)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3472289
Loyola-González OMedina-Pérez MChoo K(2020)A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and ChallengesJournal of Grid Computing10.1007/s10723-020-09526-y18:4(797-845)Online publication date: 4-Oct-2020
https://dl.acm.org/doi/10.1007/s10723-020-09526-y

On the design of hardware-software architectures for frequent itemsets mining on data streams
1. Information systems
  1. Information systems applications

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cover image Journal of Intelligent Information Systems

Journal of Intelligent Information Systems Volume 50, Issue 3

June 2018

202 pages

ISSN:0925-9902

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2018

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Cited By

Bustio-Martínez LCumplido RLetras MHernández-León RFeregrino-Uribe CHernández-Palancar J(2021)FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive ReviewACM Computing Surveys10.1145/347228954:9(1-35)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3472289
Loyola-González OMedina-Pérez MChoo K(2020)A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and ChallengesJournal of Grid Computing10.1007/s10723-020-09526-y18:4(797-845)Online publication date: 4-Oct-2020
https://dl.acm.org/doi/10.1007/s10723-020-09526-y

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