使用频繁模式列表生成封闭频繁项集

Qin Li, Sheng Chang
{"title":"使用频繁模式列表生成封闭频繁项集","authors":"Qin Li, Sheng Chang","doi":"10.1109/DBTA.2010.5658741","DOIUrl":null,"url":null,"abstract":"An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns. By performing two operations, signature vertex conjunction and vertex counting, it simplify the process of closed itemsets generation.","PeriodicalId":320509,"journal":{"name":"2010 2nd International Workshop on Database Technology and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Generating Closed Frequent Itemsets with the Frequent Pattern List\",\"authors\":\"Qin Li, Sheng Chang\",\"doi\":\"10.1109/DBTA.2010.5658741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns. By performing two operations, signature vertex conjunction and vertex counting, it simplify the process of closed itemsets generation.\",\"PeriodicalId\":320509,\"journal\":{\"name\":\"2010 2nd International Workshop on Database Technology and Applications\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Database Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DBTA.2010.5658741\",\"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 2nd International Workshop on Database Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBTA.2010.5658741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

提出了一种用简单的线性表结构发现事务数据库中封闭频繁项集的方法——频繁模式表。该方法从候选项集中选择表示模式,减少频繁模式的组合空间。通过执行签名顶点合取和顶点计数两个操作,简化了闭项集的生成过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Generating Closed Frequent Itemsets with the Frequent Pattern List
An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns. By performing two operations, signature vertex conjunction and vertex counting, it simplify the process of closed itemsets generation.
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
期刊最新文献
SRJA: Iceberg Join Processing in Wireless Sensor Networks A New Method of Selecting Pivot Features for Structural Correspondence Learning in Domain Adaptive Sentiment Analysis Apply of Data Ming Technology in CRM A New Like Fibonacci Sequence and Its Properties Multisensor Estimation Fusion for Wireless Networks with Mixed Data Delays
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1