基于改进关联规则算法的中医治疗瘿病用药规律挖掘  

Mining of Medication Rules of Traditional Chinese Medicine for Gall Disease Based on Improved Association Rule Algorithm

作　　者：杨文敏 佘侃侃[1] 叶丹 YANG Wen-min;SHE Kan-kan;YE Dan(School of Artificial Intelligence and Information Technology,Nanjing University of Traditional Chinese Medicine;Nanjing Institute of Software Technology,Nanjing 210023,China)

机构地区：[1]南京中医药大学人工智能与信息技术学院 [2]中科南京软件技术研究院,江苏南京210023

出　　处：《软件导刊》2023年第5期165-170,共6页Software Guide

基　　金：国家自然科学基金青年科学基金项目(82004498);南京中医药大学自然科学基金青年项目(NZY82004498)。

摘　　要：为挖掘中医治疗瘿病用药规律并分析关联规则算法在中医药数据上的表现,鉴于传统Apriori算法具有候选项集多、效率低、算法复杂度高的特点,引入基于事务压缩技术和散列技术改进的Apiori算法,同时针对中医药数据事务数据库中事务重复项较多且单条事务较长的特点,提出一种改进的FP-growth算法,结合这几种改进的关联规则算法进行中医药数据挖掘分析,并绘制算法时间效率图,进行算法效率分析。研究结果表明,各算法关联规则挖掘结果一致,体现了中医治疗瘿病以理气化痰、消瘿散节的基本治则。算法效率方面,改进的FP-growth算法运行效率明显提高且随着最小支持度或最小置信度阈值的减小,算法运行时间差距越发明显。In order to mine the rule of traditional Chinese medicine for treating gall disease and analyze the performance of association rule algorithm on Chinese medicine data,the traditional Apriori algorithm has the characteristics of many candidate item sets,low efficiency and high algorithm complexity.Aiming at this feature,the improved Apriori algorithm based on transaction compression technology and hash technology is introduced.The traditional Apriori algorithm has the characteristics of more duplicate items and longer single transaction in the transaction database of Chinese medicine data,an improved FP-growth algorithm is proposed,which combines these improved association rule algorithms to conduct data mining analysis of traditional Chinese medicine,draw algorithm time efficiency graph,and analyze algorithm efficiency.The results show that the mining results of association rules of each algorithm are consistent,which reflects the basic treatment principle of traditional Chinese medicine in treating gall disease,which is based on the principle of transforming phlegm,eliminating gall and dispersing knots.In terms of algorithm efficiency,the running efficiency of the improved FP-growth algorithm is significantly improved.With the reduction of the threshold of the minimum support or minimum confidence,the running time gap of the algorithm is more obvious.

关 键 词：关联规则 APRIORI FP-GROWTH 频繁项集 中医药 瘿病 

分 类 号：R581[医药卫生—内分泌]