{"title":"关联规则在冠心病数据中的相关性研究及应用","authors":"Z. Lin, Weiguo Yi, Mingyu Lu, Zhi Liu, Hao Xu","doi":"10.1109/SoCPaR.2009.39","DOIUrl":null,"url":null,"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.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"231 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease\",\"authors\":\"Z. Lin, Weiguo Yi, Mingyu Lu, Zhi Liu, Hao Xu\",\"doi\":\"10.1109/SoCPaR.2009.39\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":284743,\"journal\":{\"name\":\"2009 International Conference of Soft Computing and Pattern Recognition\",\"volume\":\"231 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference of Soft Computing and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SoCPaR.2009.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference of Soft Computing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SoCPaR.2009.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease
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.
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