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Journal of Chinese Integrative Medicine ›› 2010, Vol. 8 ›› Issue (10): 911-916.doi: 10.3736/jcim20101002

Special Issue: Traditional Chinese Medicine

• Review • Previous Articles     Next Articles

Methodology study of classification algorithm in traditional Chinese medicine syndrome study

 Min Zhoua, Na Chub, Jie Lic   

  1. a Center of Traditional Chinese Medicine Information Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
    b Department of Computer Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    c Research Institute of Traditional Chinese Medicine Literature, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
  • Received:2010-04-29 Accepted:2010-06-01 Online:2010-10-20 Published:2010-10-15

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Study of traditional Chinese medicine (TCM) syndromes is a key to the research of TCM modernization, and the core is the classification and diagnostic criteria of syndromes. The purpose of this article is to review the usage of classification algorithms of data mining in TCM syndrome researches, and comprehensively analyze the main features of algorithms and their applications. The appropriate classification algorithm should be chosen according to different research purposes. Rough sets and cluster analysis are suitable for exploratory research without requiring a prior knowledge. Fuzzy sets theory, neural networks and decision tree are suitable for syndrome diagnostic criteria research when the classification goal is clear, because they require a prior knowledge. Among them, fuzzy sets theory could be used in combination with other classification algorithms. Thus, some new methods such as fuzzy clustering, fuzzy rough sets or fuzzy decision tree might be more suitable for TCM algorithm classification research. It is suggested that some novel classification algorithms need to be developed to fit the condition of TCM syndrome, based on the interdisciplinary theories and technologies.

Key words: Syndrome, Data interpretation, Statistical, Classification, Statistical Computing

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