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Application of Apriori and FP-growth algorithms in soft examination data analysis.

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  • Additional Information
    • Author-Supplied Keywords:
      Apriori algorithm
      data minin
      FP-Growth algorithm
    • Abstract:
      With the continuous development of internet and information technology, human beings need to process a lot of information and data. When processing a large amount of information, data mining technology must be used. In order to better mine the required data information quickly based on condition matching, an optimized Apriori and FP - Growth association rule mining algorithm is proposed. Based on the algorithm flow and evaluation model, an optimization and up-date scheme is proposed, an effective data transmission evaluation model is established by effectively evaluating the state of data analysis, and the corresponding evaluation results are given. By introducing the idea of improved decomposition database to reduce the collection of infrequent databases, the algorithm adaptability is improved. In order to verify the feasibility and reliability of the method, the case experiment is demonstrated. Based on the experimental results, the algorithm is more effective in actual operation efficiency and data mining precision. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1Department of Information and Engineer, Shandong University of Science and Technology, Taian, Shandong, China
      2Department of Economic and Management, Shandong University of Science and Technology, Taian, Shandong, China
    • Full Text Word Count:
      4411
    • ISSN:
      1064-1246
    • Accession Number:
      10.3233/JIFS-179097
    • Accession Number:
      137413830
  • Citations
    • ABNT:
      YANG, X. et al. Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems, [s. l.], v. 37, n. 1, p. 425–432, 2019. DOI 10.3233/JIFS-179097. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=137413830&custid=s8280428. Acesso em: 3 jul. 2020.
    • AMA:
      Yang X, Lin X, Lin X, Yuan X, Elhoseny M. Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems. 2019;37(1):425-432. doi:10.3233/JIFS-179097.
    • AMA11:
      Yang X, Lin X, Lin X, Yuan X, Elhoseny M. Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems. 2019;37(1):425-432. doi:10.3233/JIFS-179097
    • APA:
      Yang, X., Lin, X., Lin, X., Yuan, X., & Elhoseny, M. (2019). Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems, 37(1), 425–432. https://doi.org/10.3233/JIFS-179097
    • Chicago/Turabian: Author-Date:
      Yang, Xiaodong, Xiaoxia Lin, Xiaole Lin, Xiaohui Yuan, and Mohamed Elhoseny. 2019. “Application of Apriori and FP-Growth Algorithms in Soft Examination Data Analysis.” Journal of Intelligent & Fuzzy Systems 37 (1): 425–32. doi:10.3233/JIFS-179097.
    • Harvard:
      Yang, X. et al. (2019) ‘Application of Apriori and FP-growth algorithms in soft examination data analysis’, Journal of Intelligent & Fuzzy Systems, 37(1), pp. 425–432. doi: 10.3233/JIFS-179097.
    • Harvard: Australian:
      Yang, X, Lin, Xiaoxia, Lin, Xiaole, Yuan, X & Elhoseny, M 2019, ‘Application of Apriori and FP-growth algorithms in soft examination data analysis’, Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 425–432, viewed 3 July 2020, .
    • MLA:
      Yang, Xiaodong, et al. “Application of Apriori and FP-Growth Algorithms in Soft Examination Data Analysis.” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, July 2019, pp. 425–432. EBSCOhost, doi:10.3233/JIFS-179097.
    • Chicago/Turabian: Humanities:
      Yang, Xiaodong, Xiaoxia Lin, Xiaole Lin, Xiaohui Yuan, and Mohamed Elhoseny. “Application of Apriori and FP-Growth Algorithms in Soft Examination Data Analysis.” Journal of Intelligent & Fuzzy Systems 37, no. 1 (July 2019): 425–32. doi:10.3233/JIFS-179097.
    • Vancouver/ICMJE:
      Yang X, Lin X, Lin X, Yuan X, Elhoseny M. Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems [Internet]. 2019 Jul [cited 2020 Jul 3];37(1):425–32. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=137413830&custid=s8280428