Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease

Author

Lin, Zheng-kui ; Yi, Wei-Guo ; Lu, Ming-Yu ; Liu, Zhi ; Xu, Hao

Author_Institution

Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China

fYear

2009

fDate

4-7 Dec. 2009

Firstpage

143

Lastpage

148

Abstract

The mining association rule is an important research field in data mining. The mining association rule usually adopts this model: support, confidence, interestingness. But this model can´t measure the correlative degree between the antecedent and the consequent of the rule by ration. So we proposed a new mining model of association rules: support, coincidence, interestingness and analyzed the meaning of coincidence by instance. At last, we used this model in the data about coronary heart disease and obtained a lot of meaningful rules.

Keywords

data mining; medical computing; confidence model; coronary heart disease; data mining; interestingness model; mining association rule; support model; Association rules; Cardiac disease; Cardiology; Data mining; Hospitals; Information science; Itemsets; Mathematics; Pattern recognition; Transaction databases; association rules; coincidence; coronary heart disease; interestingness; support;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of

Conference_Location

Malacca

Print_ISBN

978-1-4244-5330-6

Electronic_ISBN

978-0-7695-3879-2

Type

conf

DOI

10.1109/SoCPaR.2009.39

Filename

5368614