标题:Application of Apriori and FP-growth algorithms in soft examination data analysis
作者:Yang, Xiaodong; Lin, Xiaoxia; Lin, Xiaole
作者机构:[Yang, Xiaodong; Lin, Xiaoxia] Shandong Univ Sci & Technol, Dept Informat & Engineer, Tai An 271019, Shandong, Peoples R China.; [Lin, Xiaole] Shand 更多
通讯作者:Lin, XX;Lin, Xiaoxia
通讯作者地址:[Lin, XX]Shandong Univ Sci & Technol, Dept Informat & Engineer, Tai An 271019, Shandong, Peoples R China.
来源:JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
出版年:2019
卷:37
期:1
页码:425-432
DOI:10.3233/JIFS-179097
关键词:Apriori algorithm; FP-Growth algorithm; data minin
摘要: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.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069454808&doi=10.3233%2fJIFS-179097&partnerID=40&md5=60d44b2c27cbd2a967d953f3b8f0efb2
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