• DocumentCode
    3261870
  • Title

    An Effective Hypergraph Clustering in Multi-Stage Data Mining of Traditional Chinese Medicine Syndrome Differentiation

  • Author

    Bo, Wang ; Ming-Wei, Zhang ; Bin, Zhang ; Wei-Jie, Wei

  • Author_Institution
    Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
  • fYear
    2006
  • fDate
    Dec. 2006
  • Firstpage
    848
  • Lastpage
    852
  • Abstract
    Traditional Chinese medicine is mysterious for its special diagnosis and treatment. In TCM, syndrome differentiation is the method of recognizing and diagnosing diseases or body imbalances in TCM. In this paper, we first give a hierarch model of differentiation syndrome in traditional Chinese medicine according to the model data mining procedure is designed to complete it. Given special data mining schema and character of high-dimensional data sets, we introduce hypergraph based on greedy algorithm in cluster and similarity measure during clustering stage. Finally, the experiment shows that the hypergraph clustering is correct and efficient, which in return could be important for association rules and diagnosis
  • Keywords
    data mining; graph theory; medical computing; medicine; pattern clustering; association rule; disease diagnosis; hypergraph clustering; multistage data mining; syndrome differentiation; traditional Chinese medicine; Clustering algorithms; Data mining; Diseases; Educational institutions; Greedy algorithms; Immune system; Information science; Medical diagnostic imaging; Partitioning algorithms; Pathogens;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2702-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2006.27
  • Filename
    4063744