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A Data Mining Tool for Massive Trajectory Data  

Lee, Jae-Gil (어바나-샴페인 일리노이 주립대학 전산학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.3, 2009 , pp. 145-153 More about this Journal
Abstract
Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.
Keywords
Data Mining; Trajectory Data; Clustering; Classification; Outlier Detection;
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