DynMDL: A Parallel Trajectory Segmentation Algorithm

Eleazar Leal, L. Gruenwald
{"title":"DynMDL: A Parallel Trajectory Segmentation Algorithm","authors":"Eleazar Leal, L. Gruenwald","doi":"10.1109/BigDataCongress.2018.00036","DOIUrl":null,"url":null,"abstract":"The purpose of trajectory segmentation algorithms is to replace an input trajectory by a sub-trajectory with fewer points than the input, but that is also a good approximation to the original trajectory. As such, trajectory segmentation is an essential pre-processing step for trajectory mining algorithms, such as clustering. Among the segmentation strategies that are commonly used for trajectory clustering is Minimum Description Length (MDL)-based segmentation, which consists in finding a sub-trajectory such that the sum of its distance to the input trajectory and its overall length is minimum. However, there are no efficient algorithms for optimal MDL-based segmentation; there are only approximate algorithms. In this work we fill this gap by proposing a parallel multicore algorithm for MDL-based trajectory segmentation. We use three real-life datasets to show that our algorithm achieves optimal MDL, and compare its performance against Traclus, the state-of-the-art approximate Description Length (DL) segmentation algorithm.","PeriodicalId":177250,"journal":{"name":"2018 IEEE International Congress on Big Data (BigData Congress)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Congress on Big Data (BigData Congress)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigDataCongress.2018.00036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

The purpose of trajectory segmentation algorithms is to replace an input trajectory by a sub-trajectory with fewer points than the input, but that is also a good approximation to the original trajectory. As such, trajectory segmentation is an essential pre-processing step for trajectory mining algorithms, such as clustering. Among the segmentation strategies that are commonly used for trajectory clustering is Minimum Description Length (MDL)-based segmentation, which consists in finding a sub-trajectory such that the sum of its distance to the input trajectory and its overall length is minimum. However, there are no efficient algorithms for optimal MDL-based segmentation; there are only approximate algorithms. In this work we fill this gap by proposing a parallel multicore algorithm for MDL-based trajectory segmentation. We use three real-life datasets to show that our algorithm achieves optimal MDL, and compare its performance against Traclus, the state-of-the-art approximate Description Length (DL) segmentation algorithm.
动态mdl:一种并行轨迹分割算法
轨迹分割算法的目的是用比输入点少的子轨迹代替输入轨迹,但这也是对原始轨迹的很好的近似。因此,轨迹分割是聚类等轨迹挖掘算法必不可少的预处理步骤。在弹道聚类中常用的分割策略是基于最小描述长度(Minimum Description Length, MDL)的分割策略,即寻找与输入轨迹距离和总长度之和最小的子轨迹。然而,目前还没有有效的算法来优化基于mdl的分割;只有近似的算法。在这项工作中,我们提出了一种基于mdl的并行多核算法来填补这一空白。我们使用三个真实的数据集来证明我们的算法实现了最优的MDL,并将其性能与Traclus(最先进的近似描述长度(DL)分割算法)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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:604180095
Book学术官方微信