基于网格索引的时空轨迹伴随模式挖掘算法

Algorithm for Mining Adjoint Pattern of Spatial-Temporal Trajectory Based on Grid Index

时空轨迹伴随模式是数据挖掘领域的一项重要研究内容。CMC(Coherent Moving Cluster)算法是一种经典的时空轨迹伴随模式挖掘算法,该算法引入了DBSCAN算法以挖掘出任意形状的簇。但是,DBSCAN聚类算法极耗时,导致CMC算法的时间效率较低。因此提出了一种基于网格索引的时空轨迹伴随模式挖掘算法MAP-G(Mining Adjoint Pattern of spatial-temporal trajectory based on the Grid index)。实验表明,MAP-G算法不仅比CMC算法具有更高的时间效率,而且能够过滤掉部分不正确的结果,因此结果也更加准确。

In the field of data mining,adjoint pattern of spatial-temporal trajectory is an important research direction.CMC（Coherent Moving Cluster）algorithm is a classical algorithm for mining adjoint pattern,and it is applied to mine clusters of arbitrary shape.However,it reduces the efficiency of the algorithm.We presented an algorithm for mining adjoint pattern of spatial-temporal trajectory called MAP-G（Mining Adjoint Pattern of spatial-temporal trajectory based on the Grid index）.The experimental results demonstrate that the proposed algorithm is more efficient compared to the CMC algorithm,and the accuracy is higher as our algorithm can filter some wrong results.