基于数据集关联挖掘和概率检验的中药药性对关联规则发现

Shang Erxin, Ye Liang, Fan Xinsheng, Tang Yuping, Duan Jinao
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引用次数: 5

摘要

中药生药对是中药与方剂之间的重要环节节点。在中医理论中,对药物的多个方面有许多描述。然而,与公式理论相比,它们似乎仍然是分散的。本文采用关联规则挖掘方法,根据药物对的结构发现有意义的关联。采用标准Apriori算法和增强Apriori算法,在包括625种药物对、347种药物和49种性质的原始药物对数据库上运行。对比分析了两种算法的结果,发现新的增强Apriori算法更适合于药物对或配方中药物之间关联的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery of Association Rules between TCM Properties in Drug Pairs by Association Mining between Datasets and Probability Tests

Crude Drug Pairs in Traditional Chinese medicine (TCM) constitute an important link node between TCM drugs and medicinal formulae. In TCM theory, there are many descriptions regarding the multiple aspects of medicines. However, they seem still scattered compared to the formulae theories. In this article, the association-rule mining method was applied to discover the meaningful associations with reference to the structure of a crude drug-pair. Standard and enhanced Apriori algorithms were applied and run on the crude drug-pair database, including 625 drug pairs, 347 drugs, and 49 properties. The results from the two algorithms were compared and analyzed, and the new enhanced Apriori algorithm was found to be more suitable for the detection of association between drugs in a drug pair or formulae.

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