{"title":"A Fast Bidirectional Method for Mining Maximal Frequent Itemsets","authors":"Chao Wang, Zhi-Wei Ni, Jun-fen Guo","doi":"10.1109/CSO.2010.105","DOIUrl":null,"url":null,"abstract":"In this paper, a fast bi-directional method and an efficient data compression method for mining maximal frequent itemsets is proposed. A flexible search method is given, which exploits the advantages of bottom-up and up-bottom strategies. The compression technique use the Prime number characteristics to transform transaction data into a positive integer and can efficiently reduce the size of transaction database. This method can mine maximal frequent itemsets according to different user-defined minimum support with only one scan of original database. Theoretical and experimental analysis shows that the proposed method is scalable and efficient for mining maximal frequent itemsets.","PeriodicalId":427481,"journal":{"name":"2010 Third International Joint Conference on Computational Science and Optimization","volume":"72 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Joint Conference on Computational Science and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSO.2010.105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a fast bi-directional method and an efficient data compression method for mining maximal frequent itemsets is proposed. A flexible search method is given, which exploits the advantages of bottom-up and up-bottom strategies. The compression technique use the Prime number characteristics to transform transaction data into a positive integer and can efficiently reduce the size of transaction database. This method can mine maximal frequent itemsets according to different user-defined minimum support with only one scan of original database. Theoretical and experimental analysis shows that the proposed method is scalable and efficient for mining maximal frequent itemsets.
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