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

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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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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (51679105, 61672261, 51409117) and Jilin Province Department of Education Thirteen Five science and technology research projects [2016] No. 432, [2017] No. JJKH20170804KJ.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, China

    Yu Jiang, Minghao Zhao, Chengquan Hu, Lili He & Hongtao Bai

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, China

    Yu Jiang, Minghao Zhao, Chengquan Hu, Lili He & Hongtao Bai

  3. School of Information Engineering, Yangzhou University, Yangzhou, China

    Jin Wang

  4. Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing, China

    Jin Wang

Authors
  1. Yu Jiang
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  2. Minghao Zhao
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  3. Chengquan Hu
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  4. Lili He
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  5. Hongtao Bai
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  6. Jin Wang
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Correspondence to Hongtao Bai.

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Jiang, Y., Zhao, M., Hu, C. et al. A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU. J Supercomput 75, 732–745 (2019). https://doi.org/10.1007/s11227-018-2297-6

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  • DOI: https://doi.org/10.1007/s11227-018-2297-6

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