Affiliation:
1. Kookmin University, Seoul, Korea and Imperial College London, London, UK
2. Simon Fraser University, Burnaby, Canada
3. Imperial College London, London, UK
Abstract
Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a
regional semantic trajectory pattern mining
problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an efficient mining algorithm, called
RegMiner
(<u>Reg</u>ional Semantic Trajectory Pattern <u>Miner</u>), which effectively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that
RegMiner
finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs
several orders of magnitude
faster than the global pattern mining algorithm.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
30 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献