TGCR: An efficient algorithm for mining swarm in trajectory databases
Yanwei Yu, Qin Wang, Jun Kuang, Jie He · 2011
Advance of positioning technology have enabled mass trajectory data of moving objects obtain more convenient. These moving objects always exists special behaviour correlation on spatio-temporal characteristics, and this information is important in some domains, such as prisoner monitoring, factory management, and the study of social behaviour. Many studies have focused on relative motion pattern mining algorithm, but the inefficiency of mining algorithms is still a problem. In this paper, we propose an efficient algorithm, Time Growth Cluster Recombinant algorithm (TGCR), for discovering swarm pattern, which is a group of relaxed aggregation moving objects. The algorithm construct maximum moving objectsets according to the clustering result of each timestamp, and record corresponding maximum time set of the maximum moving objectsets over time. TGCR employs three update rules to update candidate swarm list at each timestamp and proposes an insert rule to greatly reduce the redundant candidate items in the list. In addition, closure checking rule is presented for obtaining closed swarm patterns on fly. We performed an experimental evaluation of the correctness and efficiency of our algorithm using large synthetic data. The results of experiments demonstrate that TGCR discovers swarm patterns as same as objectGrowth algorithm and our algorithm have higher performance than objectGrowth. The further algorithm enhanced can be applicable to real-time trajectory data processing system.