Libing Wu, Kui Gong, Yanxiang He, Xiaohua Ge, Jianqun Cui
{"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}
引用次数: 10
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.
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