Advertisement

Application of a New Association Rules Mining Algorithm in the Chinese Medical Coronary Disease

  • Feng Yuan
  • Hong Liu
  • ShouQiang Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

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 Genetic algorithm Particle swarm optimization 

References

  1. 1.
    Karaboga NA (2009) New design method based on artificial bee colony algorithm for digital IIR filters[J]. J Franklin Inst, 346(4):328–348Google Scholar
  2. 2.
    Na D, Chun-Ho W, Wai-Hung I (2012) An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection.Comput math appl. 64(6):1886–1902Google Scholar
  3. 3.
    Xu CF, Duan HB (2010) Particle swarm optimission (PSO) optimized edge potential function(EPT) approach to target recognition for low altitude aircraft. Pattern Recogn Lett 31(13):1759–1772CrossRefGoogle Scholar
  4. 4.
    Wang Z, Cheng D (2009) Method and application of mining association rules based on improved genetic algorithm. J Chongqing Insts of Technol. 23(4)Google Scholar
  5. 5.
    Shiwei Y, MingWei Y, Wang K (2012) A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy 42(8):329–340Google Scholar
  6. 6.
    Riget J, Vesterstrm JS, Krink K (2002) Division of labor in particle swarm opimisation. July 11Google Scholar
  7. 7.
    Poli R, Langdon WB (2008) Extending particle swarm optimisation via genetic programming. Springer Verlag, 292–300Google Scholar
  8. 8.
    Xiaoshuang Y, Minghua J (2009) Method of association rules mining based on genetic algorithm. Softw Guide 8(10):64–66Google Scholar
  9. 9.
    Huang C (2000) TCM diagnosis and treatment Of cardiovascular division diseases. Beijing: People’s medical publishing house, 128–168Google Scholar
  10. 10.
    Zeyu C (2002) Advanced studies on the puerarin radix. J Chongqing Inst Technol 18(3):105–107Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.School of Information EngineeringCollege of Shandong Labour Union AdministratorsJinanChina
  3. 3.Center of Hear,The Second Affiliated Hospital of Shandong University of Traditional Chinese MedicineFujianChina

Personalised recommendations