Baowen Xu, Jianjiang Lu, Yingzhou Zhang, Lei Xu, Huowang Chen, Hongji Yang
{"title":"Parallel algorithm for mining fuzzy association rules","authors":"Baowen Xu, Jianjiang Lu, Yingzhou Zhang, Lei Xu, Huowang Chen, Hongji Yang","doi":"10.1109/CYBER.2003.1253467","DOIUrl":null,"url":null,"abstract":"The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.","PeriodicalId":130458,"journal":{"name":"Proceedings. 2003 International Conference on Cyberworlds","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 2003 International Conference on Cyberworlds","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBER.2003.1253467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.
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