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

  • Yu Jiang
  • Minghao Zhao
  • Chengquan Hu
  • Lili He
  • Hongtao Bai
  • Jin Wang


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.


Association rules mining FP-growth WOA13 Parallel algorithm 



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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Copyright information

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

Authors and Affiliations

  • Yu Jiang
    • 1
    • 2
  • Minghao Zhao
    • 1
    • 2
  • Chengquan Hu
    • 1
    • 2
  • Lili He
    • 1
    • 2
  • Hongtao Bai
    • 1
    • 2
  • Jin Wang
    • 3
    • 4
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge EngineeringJilin UniversityChangchunChina
  3. 3.School of Information EngineeringYangzhou UniversityYangzhouChina
  4. 4.Key Lab of Broadband Wireless Communication and Sensor Network TechnologyNanjing University of Posts and Telecommunications, Ministry of EducationNanjingChina

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