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
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