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摘要
With the increasing availability of mobile IoT (Internet of Things) devices, it becomes feasible to collect user's trajectory data to discover valuable knowledge. Unfortunately, data uncertainty in trajectory data is inherent due to measurement errors or time-discretized sampling. Because of the nature of dealing with inexact data, new mining approaches are necessary to effectively account for ambiguities in the data. This paper formalizes the concept of probabilistic gathering pattern, which is to find the moving objects that travel together during a certain time period. In contrast to exact spatio-temporal trajectory, an uncertain trajectory is an ordered sequence of random variables. The distance between two uncertain series is also a random variable. Firstly, a new expected distance similarity function is designed to cluster the moving objects. Secondly, in order to avoid considerable redundancy in the complete set of gathering patterns, a new algorithm to only mining the representative set of probabilistic gathering patterns is presented, and some pruning strategies are proposed to greatly improve the mining efficiency. Finally, the efficiency of the approaches is validated by extensive experiments.
