Abstract：Data mining algorithm based on Apriori of association rules mines data only for a class of correlated datasets. However, various datasets are very large in the real world, and how to mine data among uncorrelated datasets and how to expand the number of rules are the challenging issues. The study of Apriori algorithm of association rules basically focus on the performance optimization of algorithm and different data forms at present, which does not breakthrough the limit of the uncorrelated datasets. For this, the concepts of correlated datasets, uncorrelated datasets and compatible datasets were given in the paper, furthermore a deductive method of association rules among uncorrelated datasets based on Apriori was given in this paper, and in which deductive rules of the algorithm were given. The correctness of the algorithm was proved by construction method, and the application method was demonstrated by examples. The algorithm can realize rules deduction among correlated rules based on Apriori for uncorrelated datasets, which cannot be realized by common data mining algorithms. The algorithm expands the application field of correlated rules algorithm; meanwhile, it realizes the privacy protection in a certain extent because the rules are mined independently out on the basis of compatible datasets and have not shared original data.
张春生 庄丽艳. 基于Apriori的相容数据集间关联规则演绎方法[J]. 计算机应用, 2013, 33(10): 2796-2800.
ZHANG Chunsheng ZHUANG Liyan. Deductive method of association rules among compatible datasets based on Apriori. Journal of Computer Applications, 2013, 33(10): 2796-2800.
2013, Vol. 33 
