BitApriori:基于apriori的使用比特流的频繁项集挖掘

Thi Thanh Nhan Le, Thi Thanh Thuy Nguyen, Tae-Choong Chung
{"title":"BitApriori:基于apriori的使用比特流的频繁项集挖掘","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":"{\"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}","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

摘要

候选项集的生成、修剪和计数是Apriori频繁项集挖掘的重要步骤。不幸的是,它们的计算时间太昂贵。在本文中,我们提出了一种利用比特流来提高其速度的新方法。首先,根据计数的递减找出1项集并进行排序。通过这种方式,将创建所有属性的映射。之后,每个属性将以1位表示。最后,候选项集的生成和剪枝由逻辑运算处理,节省了大量的计算时间。对于实验,我们将我们的方法与一些基于先验的技术进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BitApriori: An Apriori-Based Frequent Itemsets Mining Using Bit Streams
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信