NNF: An Effective Approach in Medicine Paring Analysis of Traditional Chinese Medicine Prescriptions

Abstract

Medicine Paring Analysis is one of the most important tasks in the research of Traditional Chinese Medicine Prescriptions. The most essential and difficult step is to mine associations between different medicine items. This paper proposes an effective approach in solving this problem. The main contributions include: (1) proposing a novel data structure called indexed frequent pattern tree (IFPT) to maintain the mined frequent patterns (2) presenting an efficient algorithm called Nearest Neighbor First (NNF) to mine association rules from IFPT (3) designing and implementing two optimization strategies that avoid the examinations of a lot of subsets of Y that can’t be the left part of any association rule of the form X $\Rightarrow$ Y – X and thus achieving a wonderful performance and (4) conducting extensive experiments which show that NNF runs far faster than Apriori algorithm and has better scalability. And finally we demonstrate the effectiveness of this method in Medicine Paring Analysis.

This work was supported by Grant from National Science Foundation of China (60473071), Specialized Research Fund for Doctoral Program by the Ministry of Education (SRFDP 20020610007), and the grant from the State Administration of Traditional Chinese Medicine (SATCM 2003JP40). LI Chuan, PENG Jing, HU Jianjun are Ph. D Candidates at DB&KE Lab, Sichuan University. Jiang Yongguang is a Professor at Chengdu University of Traditional Chinese Medicine. And TANG Changjie is the associate author.