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.
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References
Al-Debei MM, Al-Hujran O, Al-Lozi EM et al (2018) Challenges of cloud computing adoption from the toe framework perspective. Int Jof e-Bus Res 14(3):77–94
Al-Faifi AM, Song B, Hassan MM et al (2018) Performance prediction model for cloud service selection from smart data. Future Gener Comput Syst 85:97–106
Asiaei A, Rahim NZA (2019) A multifaceted framework for adoption of cloud computing in Malaysian SMEs. J Sci Technol Policy Manag 10(3):708–750
Athey S (2017) Beyond prediction: Using big data for policy problems. Science 355(6324):483–485
Barbu A, She Y, Ding L et al (2017) Feature selection with annealing for computer vision and big data learning. IEEE Trans Pattern Anal Mach Intell 39(2):272–286
Chen D, Yue B, Guo X et al (2020) Optimal design of shielding ball parameters for post insulator of ± 1100kV UHV converter station. Gaodianya Jishu/High Volt Eng 43(10):3189–3197
Cho HJ, Hwang G (2017) Optimal design for dynamic spectrum access in cognitive radio networks under rayleigh fading. J Ind Manag Optim 8(4):821–840
Cunha RLF, Rodrigues ER, Tizzei LP et al (2017) Job Placement Advisor Based on Turnaround Predictions for HPC Hybrid Clouds. Future Gener Comput Syst 67:35–46
Deng C, Zhou Y, Jiang W et al (2017) Optimal design of inter-plant hydrogen network with purification reuse/recycle[J]. Int J Hydrogen Energy 42(31):19984–20002
Ebrahim MA, Elyan T, Wadie F et al (2017) Optimal design of RC snubber circuit for mitigating transient overvoltage on VCB via hybrid FFT/Wavelet Genetic approach. Electric Power Syst Res 143:451–461
Ghomi EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88(6):50–71
Gobbi M, Previati G, Ballo F et al (2017) Bending of beams of arbitrary cross sections—optimal design by analytical formulae. Struct Multidisciplinary Optim 55(3):827–838
Goudarzi S, Hassan WH, Anisi MH (2017) ABC-PSO for vertical handover in heterogeneous wireless networks. Neurocomputing 256:63–81
Gumaei A, Sammouda R, Al-Salman AMS et al (2019) Anti-spoofing cloud-based multi-spectral biometric identification system for enterprise security and privacy-preservation. J Parallel Distrib Comput 124:27–40
Ha H, Lee S, Kim H (2017) Optimal design of passive containment cooling system for innovative PWR. Nucl Eng Technol 49(5):941–952
Janssen M, Haiko VDV, Wahyudi A (2017) Factors influencing big data decision-making quality. J Bus Res 70:338–345
Jiang Y, Zhao M, Hu C et al (2019) A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU. J Supercomput 75(2):732–745
Kammoun A, Couillet R, Pascal F et al (2018) Optimal design of the adaptive normalized matched filter detector using regularized tyler estimators. IEEE Trans Aerosp Electron Syst 54(99):755–769
Li M, Ding D, Yi Y (2019) Data analysis of tyre quality based on improved FP-growth algorithm. Zhongguo Jixie Gongcheng/China Mech Eng 30(2):244–251
Rathore MMU, Paul A, Ahmad A et al (2020) Real-time big data analytical architecture for remote sensing application. IEEE J Sel Top Appl Earth Observ Remote Sens 8(10):4610–4621
Ren HP, Fan JT, Kaynak O (2019) Optimal design of a fractional-order proportional integer differential controller for a pneumatic position servo system. IEEE Trans Industr Electron 66(8):6220–6229
Su HT, Cheung SH, Lo YM (2020) Multi-objective optimal design for flood risk management with resilience objectives. Stoch Env Res Risk Assess 32(4):1147–1162
Wang H, Zhao Y, Ma X, Wang H (2017) Optimal design of constant-stress accelerated degradation tests using the m-optimality criterion. Reliab Eng Syst Saf 164:45–54
Wang Y, Kung LA, Byrd TA (2020) Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change 126(1):3–13
Xu F, Lu H (2017) The application of fp-growth algorithm based on distributed intelligence in wisdom medical treatment. Int J Pattern Recognit Artif Intell 31(4):232–237
Xu L, Jiang C, Wang J et al (2017) Information security in big data: privacy and data mining. IEEE Access 2(2):1149–1176
Zhang H, Wang CM, Challamel N et al (2017) Semi-analytical solutions for optimal design of columns based on Hencky bar-chain model. Eng Struct 136:87–99
Zhang Y, Qiu M, Tsai CW et al (2020) Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1):88–95
Zhao-Can LI, Kun XU (2017) Optimal design of rotary tool based on ANSYS workbench. Intl J Plant Eng Manag 22(01):59–64
Acknowledgements
This work was supported by Topic: Institute of higher vocational education, Changzhou University; Project title: Research on the application of information based intelligent classroom in Higher Vocational Education; Project ID: CDGZ2019043. Topic:Analysis and Countermeasure Research on the Application of Online Courses in Internet Plus Age. Project ID: 2020JDKT076
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Zhang, B. Optimization of FP-Growth algorithm based on cloud computing and computer big data. Int J Syst Assur Eng Manag 12, 853–863 (2021). https://doi.org/10.1007/s13198-021-01139-2
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DOI: https://doi.org/10.1007/s13198-021-01139-2