期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：11
DOI：10.15680/IJIRCCE.2015.0311142
出版社：S&S Publications
摘要：Trajectory patterns discovery is more useful in learning interactions between moving objects. Thetrajectory patterns are arranged in the order of temporal tightness. A set of trajectory patterns are discovered and theirgranularity are adjusted by split and merge to detect other types. Trajectory classification is the model construction forpredicting the class labels of moving objects based on their trajectories. Trajectory outlier detection detects outlyingline segments for trajectory outliers and partitions a trajectory into a set of line segments. The classification process isbased upon the fuzzy logic by using Naives byes classification. Groups of moving objects that have followed similarmovements in different times are proposed by the Time Relaxed Spatiotemporal Trajectory Join. Unifying trajectorypatterns (UT-patterns) of various temporal tightness is proposed in this paper. To show various patterns a pattern forestis constructed .For example, while a group of animals are migrating, some animals may leave the group or new animalsmay enter it so that a formal definition for moving clusters is provided.
