Data mining traditional Chinese medicine (TCM) lessons learnt from mining in law and allopathic medicine


Autoria(s): Stranieri, Andrew; Sahama, Tony R.
Contribuinte(s)

Song, Jian

Data(s)

2012

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

http://eprints.qut.edu.au/54262/

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