Abstract—Detecting significant places are necessary for learning patterns of human behavior. Moreover, the Global Positioning System is the high accurate estimation of positioning method for mobile tracking. In this paper, we propose a method for detecting significant places based on GPS data. It is difficult to determine significant places relying on the clusters from using distance and time thresholds because of the difference of noises. Therefore, we introduce a method based on EIDBSCAN algorithm to detect arbitrary shape clusters with different densities, namely MKEIDBSCAN. We also propose the way to estimate input parameters of density-based clustering algorithms including the radius and the minimum number of points. As a result of using our proposal, significant places are detected more accurately and the running time is reduced.
Index Terms—significant places, density-based clustering algorithm, GPS-collected points
Cite: Chuyen Luong, Son Do, Thang Hoang, and Deokjai, "A Method for Detecting Significant Places from GPS Trajectory Data," Vol. 6, No. 1, pp. 44-48, February, 2015. doi:10.12720/jait.6.1.44-48
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