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The Journal of Supercomputing

Erschienen in:

21.03.2018

A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU

verfasst von: Yu Jiang, Minghao Zhao, Chengquan Hu, Lili He, Hongtao Bai, Jin Wang

Erschienen in: The Journal of Supercomputing | Ausgabe 2/2019

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Abstract

According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000–300,000 data, which leads to more than 1.2 times of performance improvement.

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Titel: A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU
verfasst von: Yu Jiang
Minghao Zhao
Chengquan Hu
Lili He
Hongtao Bai
Jin Wang
Publikationsdatum: 21.03.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 2/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2297-6

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