中国结合医学杂志（英文版）Chinese Journal of Integrative Medicine

Author Name	Affiliation	E-mail
YUAN Feng, CHEN Shou-qiang, XU Liang
LIU Hong	1. School of Information Science and Engineering, Shandong Normal University, Jinan (250014), China 2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan (250014), China	yuanfeng623@163.com

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Abstract:

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.