Application of a New Association Rules Mining Algorithm in the Chinese Medical Coronary Disease
The paper deals with efficient mining association rules in large data sets of TCM clinical data of the coronary disease. Aiming at the problems that TCM clinical data exist a great deal of data and high association characteristics, which lead to the probl
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Application of a New Association Rules Mining Algorithm in the Chinese Medical Coronary Disease Feng Yuan, Hong Liu and ShouQiang Chen
Abstract The paper deals with efficient mining association rules in large data sets of TCM clinical data of the coronary disease. Aiming at the problems that TCM clinical data exist a great deal of data and high association characteristics, which lead to the problem of low efficiency, slow convergence and omission rules, a new combined method is proposed based on genetic algorithm and particle swarm optimization. The method designs the fitness function, uses particle swarm optimization to finish evolution and integration, and combines with genetic manipulation the advantage of simple and robust. The medical treatment records of coronary disease were verified by the experiments. Experimental results show that compared with traditional association rules mining method, combined algorithm performs better in terms of diversity of population and discovering more effective association rules. The mining result has reference value in TCM treatment of the coronary disease.
Keywords Association rules mining Traditional chinese medicine algorithm Particle swarm optimization
Genetic
F. Yuan (&) H. Liu School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China e-mail: [email protected] F. Yuan School of Information Engineering, College of Shandong Labour Union Administrators, Jinan, China S. Chen Center of Hear,The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Fujian, China
S. Li et al. (eds.), Frontier and Future Development of Information Technology in Medicine and Education, Lecture Notes in Electrical Engineering 269, DOI: 10.1007/978-94-007-7618-0_36, Springer Science+Business Media Dordrecht 2014
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36.1 Introduction Data mining refers to extracting or ‘‘mining’’ useful knowledge from large amount of data. These knowledge is impliedly, unknown before and potential useful information [1]. As a very useful knowledge model of data mining, association rules mining has received a great deal of attention and research. The key and core of the algorithm is to generate frequent item sets, so how to efficiently generate frequent item sets is a quite promising research. Genetic Algorithm (GA) and Particles Swarm Optimization (PSO) are both population based algorithms that have been proven to be successful in data mining. However, both models have strengths and weaknesses [2]. Mining genetic association rules which is one of the data mining is reconstructed and invested widely as an useful knowledge model. But there are still some abuses in service use of genetic association rules algorithm [3]. GA is a population-based evolutionary algorithm which is random, robustness and implicit parallelism, it can search for global optimization quickly and efficiently, and is a effective method of processing large set of data items [4]. Compared with GA, PSO has a much more profound intelligent back
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