Libing Wu, Kui Gong, Yanxiang He, Xiaohua Ge, Jianqun Cui
{"title":"改进Apriori算法的研究","authors":"Libing Wu, Kui Gong, Yanxiang He, Xiaohua Ge, Jianqun Cui","doi":"10.1109/IWISA.2010.5473450","DOIUrl":null,"url":null,"abstract":"The Apriori algorithm is one of the most influential apriori for mining association rules. The basic idea of the Apriori algorithm is to identify all the frequent sets. Through the frequent sets, derived association rules, these rules must satisfy minimum support threshold and minimum confidence threshold. This paper presents improved algorithms, mainly through the introduction of interest items, frequency threshold, to improve the mining efficiency, dynamic data mining to facilitate the needs of users. Confirmed by many experiments, this algorithm is better than traditional algorithms in time and space complexity.","PeriodicalId":298764,"journal":{"name":"2010 2nd International Workshop on Intelligent Systems and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A Study of Improving Apriori Algorithm\",\"authors\":\"Libing Wu, Kui Gong, Yanxiang He, Xiaohua Ge, Jianqun Cui\",\"doi\":\"10.1109/IWISA.2010.5473450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Apriori algorithm is one of the most influential apriori for mining association rules. The basic idea of the Apriori algorithm is to identify all the frequent sets. Through the frequent sets, derived association rules, these rules must satisfy minimum support threshold and minimum confidence threshold. This paper presents improved algorithms, mainly through the introduction of interest items, frequency threshold, to improve the mining efficiency, dynamic data mining to facilitate the needs of users. Confirmed by many experiments, this algorithm is better than traditional algorithms in time and space complexity.\",\"PeriodicalId\":298764,\"journal\":{\"name\":\"2010 2nd International Workshop on Intelligent Systems and Applications\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Intelligent Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWISA.2010.5473450\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Workshop on Intelligent Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWISA.2010.5473450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Apriori algorithm is one of the most influential apriori for mining association rules. The basic idea of the Apriori algorithm is to identify all the frequent sets. Through the frequent sets, derived association rules, these rules must satisfy minimum support threshold and minimum confidence threshold. This paper presents improved algorithms, mainly through the introduction of interest items, frequency threshold, to improve the mining efficiency, dynamic data mining to facilitate the needs of users. Confirmed by many experiments, this algorithm is better than traditional algorithms in time and space complexity.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
