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Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

机译：基于OLAP的多维关联规则挖掘方法的研究与应用

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摘要

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multidimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.
机译：关于数据挖掘效率低的问题，提出了基于大学大数据环境中传统多维关联规则的低维度和低准确性，提出了一种基于OLAP的多维关联规则挖掘方法，将哈希函数和标记的交易压缩技术结合在一起解决APRIORI算法中过多或冗余候选集的问题，并在线分析处理用于管理关联挖掘过程中的中间数据，以减少重复计算引起的时间开销。为了验证所提出的方法的有效性，在高校领域建设了一个学习情况分析系统。多维关联规则挖掘方法用于分析超过21,000个脱敏的实际数据，以便挖掘影响学生学业成绩的关键因素。实验结果表明，所提出的多维采矿模型具有良好的采矿成果，显着提高了时间性能。

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