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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
Centrality Research on the Traditional Chinese Medicine Network
Adelaide, Australia
January 23-January 24
ISBN: 0-7695-3090-7
ASCII Text
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Zhang Dezheng, Gao Lixin, Zhang Huansheng, Liu Jianming, "Centrality Research on the Traditional Chinese Medicine Network," International Workshop on Knowledge Discovery and Data Mining, pp. 59-62, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.
 
 
BibTex
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@article{ 10.1109/WKDD.2008.19,
author = {Zhang Dezheng and Gao Lixin and Zhang Huansheng and Liu Jianming},
title = {Centrality Research on the Traditional Chinese Medicine Network},
journal ={International Workshop on Knowledge Discovery and Data Mining},
volume = {0},
year = {2008},
isbn = {0-7695-3090-7},
pages = {59-62},
doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.19},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Workshop on Knowledge Discovery and Data Mining
TI - Centrality Research on the Traditional Chinese Medicine Network
SN - 0-7695-3090-7
SP59
EP62
A1 - Zhang Dezheng,
A1 - Gao Lixin,
A1 - Zhang Huansheng,
A1 - Liu Jianming,
PY - 2008
VL - 0
JA - International Workshop on Knowledge Discovery and Data Mining
ER -
 
Aiming at the complex data in the Traditional Chinese Medicine, a new way is proposed in this paper that data mining of complex relations to find out the potential information among different medicine objects. We turned the Traditional Chinese Medicine knowledge network into graph by using information from ontology, then adopted centrality algorithm to analyze and process this graph, and finally mined valuable medicine knowledge. As the result of the verification test, this algorithm shows very good practicability.
Citation:
Zhang Dezheng, Gao Lixin, Zhang Huansheng, Liu Jianming, "Centrality Research on the Traditional Chinese Medicine Network," wkdd, pp.59-62, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008
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