LCGMiner: levelwise closed graph pattern mining from large databases

Aihua Xu, H. Lei
{"title":"LCGMiner: levelwise closed graph pattern mining from large databases","authors":"Aihua Xu, H. Lei","doi":"10.1109/SSDBM.2004.47","DOIUrl":null,"url":null,"abstract":"LCGMiner (levelwise closed graph pattern miner) is proposed to improve CloseGraph (Yan and Han, 2003) in discovering frequent closed sub graphs. Frequent closed edgesets with the same extended vertexsets are expanded in pattern generation compared to one edge or one vertex in traditional methods. Experiments on synthetic datasets as well as a real NIH dataset demonstrates that our algorithm outperforms CloseGraph and gSpan.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSDBM.2004.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

LCGMiner (levelwise closed graph pattern miner) is proposed to improve CloseGraph (Yan and Han, 2003) in discovering frequent closed sub graphs. Frequent closed edgesets with the same extended vertexsets are expanded in pattern generation compared to one edge or one vertex in traditional methods. Experiments on synthetic datasets as well as a real NIH dataset demonstrates that our algorithm outperforms CloseGraph and gSpan.
LCGMiner:从大型数据库中分层封闭图形模式挖掘
LCGMiner (levelwise closed graph pattern miner)是为了改进CloseGraph (Yan and Han, 2003)在发现频繁闭子图方面的能力而提出的。与传统方法相比,具有相同扩展顶点集的频繁闭边集在模式生成中得到了扩展。在合成数据集和真实NIH数据集上的实验表明,我们的算法优于CloseGraph和gSpan。
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