组移动模式挖掘中轨迹聚类的置信区间法

Based on the spatial data mining algorithms, the temporal distance and average distance of moving objects are defined in this paper, and then sample variance approach and confidence-interval approach for trajectory clustering are provided. The two approaches can discover all the object pairs that have similar trajectories at certain time intervals. Using different sampling granularities of trajectory distance can greatly depress the time complexity of the trajectory clustering algorithm. The clustering algorithm based on sample variance approach and confidence-interval approach is tested both on synthetic and real datasets. It is indicated that the two approaches can also be used as pretreatment methods for other trajectory clustering algorithms, and can greatly reduce the data amount being searched.