Suhyun Kang, Ki Yong Lee

KIPS Transactions on Software and Data Engineering, Vol. 8, No. 12, pp. 473-482, Dec. 2019
10.3745/KTSDE.2019.8.12.473,   PDF Download:   Full Text
Keywords: Trajectory Data Stream, Companion Groups Detection, Stream Data Mining
Abstract

There have already been studies analyzing the trajectories of objects from data streams of moving objects. Among those studies, there are also studies to discover groups of objects that move together, called companion groups. Most studies to discover companion groups use existing clustering techniques to find groups of objects close to each other. However, these clustering-based methods are often difficult to find the right companion groups because the number of clusters is unpredictable in advance or the shape or size of clusters is hard to control. In this study, we propose a new method that discovers companion groups based on the distance specified by the user. The proposed method does not apply the existing clustering techniques but periodically determines the groups of objects close to each other, by using a technique that efficiently finds the groups of objects that exist within the user-specified distance. Furthermore, unlike the existing methods that return only companion groups and their trajectories, the proposed method also returns their appearance and disappearance time. Through various experiments, we show that the proposed method can detect companion groups correctly and very efficiently.

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