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
Link To Document :
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