Thi Thanh Nhan Le, Thi Thanh Thuy Nguyen, Tae-Choong Chung
{"title":"BitApriori: An Apriori-Based Frequent Itemsets Mining Using Bit Streams","authors":"Thi Thanh Nhan Le, Thi Thanh Thuy Nguyen, Tae-Choong Chung","doi":"10.1109/ICISA.2010.5480373","DOIUrl":null,"url":null,"abstract":"Generating, pruning and counting itemset candidates are important steps in Apriori frequent itemset mining. Unfortunately, their computation time are too expensive. In this paper, we propose a new method using Bit Stream to improve their speed. At the begining, the 1-itemsets are found out and sorted according to the decline of count. By that way, a map of all attributes would be created. After that, each attribute will be presented by 1 bit. At last, the generating and pruning itemset candidates are processed by LOGIC operations which are not cost much of computation time. For experiments we compare our method with some Apriori-based state of the arts.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Information Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISA.2010.5480373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Generating, pruning and counting itemset candidates are important steps in Apriori frequent itemset mining. Unfortunately, their computation time are too expensive. In this paper, we propose a new method using Bit Stream to improve their speed. At the begining, the 1-itemsets are found out and sorted according to the decline of count. By that way, a map of all attributes would be created. After that, each attribute will be presented by 1 bit. At last, the generating and pruning itemset candidates are processed by LOGIC operations which are not cost much of computation time. For experiments we compare our method with some Apriori-based state of the arts.