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Optimization of FP-Growth algorithm based on cloud computing and computer big data

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  • Baohua Zhang

    (Changzhou Vocational Institute of Engineering)

Abstract

The rapid development of cloud computing technology has spawned many excellent cloud computing platforms. These cloud computing platforms provide an effective solution for the processing of big data, which can be used as the basis for the study of parallel mining algorithms and the application of algorithms. This article uses the FP-Growth algorithm to mine and analyze computer big data. Aiming at the low extraction efficiency of traditional FP-Growth algorithm in large-scale data environment, an improved FP-Growth algorithm is proposed. In addition, in view of the shortcomings of frequent lists of L elements that are often cross-referenced in the FP-tree construction process, an improved algorithm based on hash tables is proposed, which realizes the storage address processing element name key, and then realizes the element name key to storage numbered mapping. This article mainly introduces the optimization of FP-Growth algorithm under the background of cloud computing and computer big data. The experimental results in this paper show that the performance of the improved FP-gtowth algorithm is better than the original algorithm, the traversal time is reduced by 13%, and the mining efficiency is increased by 25%. In addition, the use of this algorithm for data clustering reduces the error rate and optimizes performance becomes better and has better application value.

Suggested Citation

  • Baohua Zhang, 2021. "Optimization of FP-Growth algorithm based on cloud computing and computer big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 853-863, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01139-2
    DOI: 10.1007/s13198-021-01139-2
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    References listed on IDEAS

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    1. Wang, Han & Zhao, Yu & Ma, Xiaobing & Wang, Hongyu, 2017. "Optimal design of constant-stress accelerated degradation tests using the M-optimality criterion," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 45-54.
    2. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    3. Omar Al-Hujran & Enas M. Al-Lozi & Mutaz M. Al-Debei & Mahmoud Maqableh, 2018. "Challenges of Cloud Computing Adoption From the TOE Framework Perspective," International Journal of E-Business Research (IJEBR), IGI Global, vol. 14(3), pages 77-94, July.
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