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

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.