摘要
为了提高数据挖掘算法在中医药数据处理中的效率,提出了采用TCMA算法挖掘TCM-FP树中的维间最大频繁项集。根据中医药数据的特点及药组挖掘的需求,在FP-growth算法的基础上,提出了TCM-FP树及其建树算法和挖掘算法TCMA,在TCM-FP树中采用优化搜索策略挖掘维间最大频繁项集,与FP-growth算法挖掘所有频繁项集比,大大缩短了时间。优化搜索算法切合中药TCM规则挖掘的实际意义,比FP-growth算法挖掘有更高的运行效率。
To improve the data mining algorithms in traditional Chinese medicine data processing efficiency,the adoption of the inter-dimensional maximal frequent item set of the TCMA（traditional Chinese medicine algorithm） algorithms mining is presented.In accordance with the characteristics of Chinese medicine and the demand for drugs group and based on FP-growth algorithms,TCM-FP tree and its contribution algorithm and TCMA mining algorithm is proposed.Optimized search strategy mining inter-dimensional maximal frequent item sets of the TCM-FP tree is put forward.FP-growth algorithm with all the frequent item sets mining ratio,significantly shortens the time.Optimization search algorithm can meet the practical meaning of TCM rules mining,and is more efficient than mining operation of FP-growth algorithm and has good application value.
出处
《江南大学学报（自然科学版）》
CAS
2010年第2期185-190,共6页
Joural of Jiangnan University (Natural Science Edition)
