Towards a Verified Parallel Implementation of Frequent Itemset Mining

C. Whitney, F. Loulergue
{"title":"Towards a Verified Parallel Implementation of Frequent Itemset Mining","authors":"C. Whitney, F. Loulergue","doi":"10.1109/HPCS.2017.138","DOIUrl":null,"url":null,"abstract":"Information technologies have allowed for the rapid growth of both data acquisition and data storage. With this growth comes the challenge of extracting useful information. One piece of information that is interesting to academics and industry is the relationships between items in a large data set. One approach is to find the relationships between items by calculating how frequently the items appear together in a subset. This is known as the frequent itemset mining problem. The problem goes as follows, given a database with sets of items, find the items that occur frequently together in a subset.","PeriodicalId":115758,"journal":{"name":"2017 International Conference on High Performance Computing & Simulation (HPCS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCS.2017.138","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

Information technologies have allowed for the rapid growth of both data acquisition and data storage. With this growth comes the challenge of extracting useful information. One piece of information that is interesting to academics and industry is the relationships between items in a large data set. One approach is to find the relationships between items by calculating how frequently the items appear together in a subset. This is known as the frequent itemset mining problem. The problem goes as follows, given a database with sets of items, find the items that occur frequently together in a subset.
一种经过验证的频繁项集挖掘并行实现
信息技术使数据采集和数据存储都能迅速增长。这种增长带来了提取有用信息的挑战。学术界和工业界感兴趣的一条信息是大型数据集中项目之间的关系。一种方法是通过计算项目在子集中一起出现的频率来查找项目之间的关系。这被称为频繁项集挖掘问题。问题是这样的,给定一个包含项目集的数据库,找出子集中经常出现的项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:481959085
Book学术官方微信