Nowadays, location-aware devices, such as GPS, provide a huge volume of spatial-temporal data. Analyzing the data to understand the behavior of objects (e.g. people) could be beneficial in many application areas. Due to the spatial and temporal nature and their complexity, researchers have developed various data mining techniques such as trajectory segmentation, which splits the trajectories into sub-trajectories, to prepare them for the mining step. A central issue in discovering knowledge is choosing an appropriate trajectory segmentation technique. In this paper, we provide a comparative study on two trajectory segmentation techniques, density-based and grid-based, when applied to sequential patterns discovery. We conducted experiments using two real-life datasets to evaluate the performance of the methods in terms of execution time and their impact on discovering the sequential patterns. The experimental results showed that the density-based is more efficient, while the grid-based is more effective.
|Original language||English (US)|
|Title of host publication||Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018|
|Editors||Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Jan 22 2019|
|Event||2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States|
Duration: Dec 10 2018 → Dec 13 2018
|Name||Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018|
|Conference||2018 IEEE International Conference on Big Data, Big Data 2018|
|Period||12/10/18 → 12/13/18|
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.
© 2018 IEEE.
- Sequential trajectory patterns
- Trajectory segmentation