DBSCAN Revisited, Revisited

Erich Schubert, J. Sander, M. Ester, H. Kriegel, Xiaowei Xu
{"title":"DBSCAN Revisited, Revisited","authors":"Erich Schubert, J. Sander, M. Ester, H. Kriegel, Xiaowei Xu","doi":"10.1145/3068335","DOIUrl":null,"url":null,"abstract":"At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.","PeriodicalId":6983,"journal":{"name":"ACM Transactions on Database Systems (TODS)","volume":"17 1","pages":"1 - 21"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"966","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Database Systems (TODS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3068335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 966

Abstract

At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.
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
再来,再来
在SIGMOD 2015上,一篇题为“DBSCAN重访:错误声明、不可修复性和近似”的文章获得了会议最佳论文奖。在这篇技术通信中,我们想指出DBSCAN表示方式中的一些不准确之处,以及为什么批评应该针对空间索引结构(如r树)的性能假设,而不是针对可以使用这些索引的算法。我们还将讨论DBSCAN性能与数据集可索引性的关系,并讨论选择适当的DBSCAN参数的一些启发式方法。本文将提出一些不良参数的指标,以帮助指导该算法未来的用户选择参数,如获得有意义的结果和良好的性能。在新的实验中,我们表明,如果DBSCAN参数选择得当,新的SIGMOD 2015方法似乎不会提供实际好处,因此它们主要具有理论意义。综上所述,具有有效指标和合理参数选择的原始DBSCAN算法与Gan和Tao提出的方法相比具有竞争力。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
On Finding Rank Regret Representatives Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration Persistent Summaries Influence Maximization Revisited: Efficient Sampling with Bound Tightened The Space-Efficient Core of Vadalog
×
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