关键词: 目标轨迹数据, 伴随模式挖掘, 密度聚类, 关联分析, 群体运动模式

Abstract: At present, the mainstream trajectory adjoint pattern mining methods are usually for short time analysis, and most of them mine trajectory data once, rarely taking into account the relevant analysis between before and after discontinuous time, so the implicit adjoint pattern mining is not accurate. This paper analyzes the trajectory adjoint pattern, and puts forward an adjoint pattern mining method based on density clustering and association analysis. Firstly, the local pattern clusters in the trajectory data are mined, and the mining results are optimized by the association analysis of the local pattern clusters in discontinuous time slices. Experimental results show that the method can effectively and accurately mine the adjoint model of the trajectory.

Key words: target trajectory data, adjoint pattern mining, density clustering, association analysis, population movement model