关键词: 网格热度值, 路径提取, 数据挖掘, 轨迹聚类, 密度聚类

Abstract: With the development of moving target location technology like GPS, wireless sensor and satellite, a large amount of mobile data such as human walking trajectory, vehicle trajectory, ship trajectory and so on is generated. However, moving target detection device can only store information of a series of discrete points. Therefore, using the aid of discrete points to track and recover the full path is the necessary prerequisite to grasp the rule of moving target. Using the method of date mining can find the regular path from the historical information of moving target, while the clustering method based on the grid can not only effectively express these trajectories, but also analyze the relationship among these points, and it is an effective method for extraction of path. At present, the research of trajectory clustering is mostly from the perspective of space or time，by means of density clustering method to find out hot paths. These paths are often the discrete path fragments, which are not able to effectively express the continuous path of moving target with different shapes. In this paper, the method of heat factor similarity measurement based on the combination of distance and density of grid heat value is proposed. Finally, the actual automatic identification system (AIS) dynamic data is used to verify the accuracy and performance of the algorithm. The algorithm analysis and experimental results show that the regular path extraction algorithm based on grid heat value proposed in this paper can effectively find out different trajectory sequences of different shapes.

Key words: grid heat value, path extraction, data mining, trajectory clustering, density-based clustering