{"title":"一种新的因果分析关联规则","authors":"Zhefu Yu, Huibiao Lu, Chuanying Jia","doi":"10.1109/JCAI.2009.36","DOIUrl":null,"url":null,"abstract":"A new association rule algorithm is discussed. It is based on the weighted association rule algorithms of minwal(0) and minwal(w).The new algorithm can effectively mine the association rules which define some attributes as antecedent partial, while others as consequent partial. The new algorithm also can effectively mine the association rules with lower support and high confidence, and these association rules are greater significant in some applications.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Association Rule for Causes and Effects Analysis\",\"authors\":\"Zhefu Yu, Huibiao Lu, Chuanying Jia\",\"doi\":\"10.1109/JCAI.2009.36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new association rule algorithm is discussed. It is based on the weighted association rule algorithms of minwal(0) and minwal(w).The new algorithm can effectively mine the association rules which define some attributes as antecedent partial, while others as consequent partial. The new algorithm also can effectively mine the association rules with lower support and high confidence, and these association rules are greater significant in some applications.\",\"PeriodicalId\":154425,\"journal\":{\"name\":\"2009 International Joint Conference on Artificial Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Joint Conference on Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/JCAI.2009.36\",\"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 Joint Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCAI.2009.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Association Rule for Causes and Effects Analysis
A new association rule algorithm is discussed. It is based on the weighted association rule algorithms of minwal(0) and minwal(w).The new algorithm can effectively mine the association rules which define some attributes as antecedent partial, while others as consequent partial. The new algorithm also can effectively mine the association rules with lower support and high confidence, and these association rules are greater significant in some applications.
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