Data mining traditional Chinese medicine (TCM) lessons learnt from mining in law and allopathic medicine
Contribuinte(s) |
Song, Jian |
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Data(s) |
2012
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Resumo |
Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theorectical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/54262/5/54262.pdf http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=19883 Stranieri, Andrew & Sahama, Tony R. (2012) Data mining traditional Chinese medicine (TCM) lessons learnt from mining in law and allopathic medicine. In Song, Jian (Ed.) Proceedings of the 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), IEEE, Beijing, China. |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #089900 OTHER INFORMATION AND COMPUTING SCIENCES #Component #Data mining #Traditional Chinese Medicine #Law #Health informatics |
Tipo |
Conference Paper |