基于属性归纳的中药方剂数据挖掘

Traditional Chinese Medicine prescription mining based on attribute-oriented relevancy induction

传统的面向属性归纳技术(AOI)存在概化粗糙及算法效率较低等缺陷。为适应中药方剂数据挖掘的复杂需求,提出基于中药数据驱动的属性关联概化算法;为关联的维度创建概念树,利用关联属性与基准属性的相关性以提高归纳的效率,实现了面向属性关联归纳的数据挖掘系统TCMDBMiner。实验结果表明,新算法较传统算法的归纳概化效率提高了23%以上,挖掘结果符合中医理论。

The traditional Attribute-Oriented Induction （AOI） technique is weak at efficiency and coarseness of generalization. In order to satisfy the complex requirements in Chinese medicine prescript/on mining, this study proposed a new algorithm based on attribute relevancy generalizing driven by Chinese medicine data, established concept-tree for relevancy-dimension, utilized pertinence of relevancy-attribute and benchmark-attribute to enhance efficiency of induction, and implemented a new data mining system TCMDBMiner based on attribute-oriented relevancy induction. Experimental results show that new algorithm is 23% faster than traditional method and the mining results are consistent with the Chinese medicine theory.