动态mdl:一种并行轨迹分割算法

Eleazar Leal, L. Gruenwald
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引用次数: 6

摘要

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。
DynMDL: A Parallel Trajectory Segmentation Algorithm
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.
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