Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories
Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories
复制标题
DOI：
10.5220/0004543401430151
复制
发表时间：
2017-01
期刊：
影响因子：
--
通讯作者：
Xiaoliang Geng;T. Uno;Hiroki Arimura
中科院分区：
文献类型：
--
作者：
Xiaoliang Geng;T. Uno;Hiroki Arimura
在本文中，我们基于深度优先频繁项集挖掘方法，如Eclat （Zaki, 2000）或LCM (Uno et al., 2004)，实现了用于挖掘群体模式的深度优先算法的最新理论进展（Arimura等，2013）。群模式是一类时空模式，表示一组在给定时间段内彼此接近的移动物体（Gudmundsson和van Kreveld， Proc. ACM GIS ' 06; Benkert, Gudmundsson, Hubner, Wolle, Computational Geometry, 41:11, 2008）。我们实现了一个基本算法的两个扩展，一个用于一类封闭模式，称为向右最大长度群模式，另一个使用几何索引的加速技术。为了评估这些扩展，我们在合成数据集上进行了实验。实验表明，在大多数参数设置上，改进后的算法比原算法快几个数量级。
In this paper, we implement recent theoretical progress of depth-first algorithms for mining flock patterns (Arimura et al., 2013) based on depth-first frequent itemset mining approach, such as Eclat (Zaki, 2000) or LCM (Uno et al., 2004). Flock patterns are a class of spatio-temporal patterns that represent a groups of moving objects close each other in a given time segment (Gudmundsson and van Kreveld, Proc. ACM GIS’06; Benkert, Gudmundsson, Hubner, Wolle, Computational Geometry, 41:11, 2008). We implemented two extensions of a basic algorithm, one for a class of closed patterns, called rightward length-maximal flock patterns, and the other with a speed-up technique using geometric indexes. To evalute these extensions, we ran experiments on synthesis datasets. The experiments demonstrate that the modified algorithms with the above extensions are several order of magnitude faster than the original algorithm in most parameter settings.