The method includes using TBapriori algorithm to generate strong association rules, the algorithm of sorting rule groups, multiple imputation, statistics and analysis.
该方法主要包括由TBapriori算法产生强关联规则,、规则组排序,多重填补,统计分析等四部分组成。
参考来源 - 基于关联规则挖掘算法的改进及其应用研究·2,447,543篇论文数据，部分数据来源于NoteExpress
In combination single imputation of missing data with multiple imputation, a new missing data imputation—KNNMI is proposed.
综合数据缺失值的单一填补和多重填补方法，提出一种新的信用指标缺失值填补方法—KNNMI。
RESULTS: The multiple imputation method imputed missing values of the crossover design and generated valid statistical inferences.
结果：多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
Results The multiple imputation method can impute missing values of the crossover design and generate valid statistical inferences.
结果多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
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