基于时空轨迹的移动对象汇聚模式挖掘算法

移动对象的聚集模式是时空轨迹模式挖掘中的重要课题，它研究移动对象群体在多个连续时刻中的空间聚集问题。现有的聚集模式基于共现模式进行定义，挖掘结果中夹杂大量非运动的聚集群体，严重影响模式挖掘的效果。为了解决此问题，本文提出了基于群体运动过程建模的汇聚模式。该模式定义从群体运动形态出发进行设计，准确识别向心运动的移动群体，有效排除非聚集类型运动群体的干扰。本文设计并实现了汇聚模式挖掘（Converging pattern mining，CPM）算法，该算法首先定位密度峰值点，确定候选的汇聚中心区域，然后依次识别每个时刻的汇聚群体，按照群体汇聚的持续性要求识别汇聚模式。基于真实轨迹数据进行实验，结果验证了本文提出的CPM算法在挖掘效果和算法效率的有效性。

Algorithm for Mining Converging Patterns of Moving Objects from Spatiotemporal Trajectories

Gathering pattern is an important research topic in the field of trajectory pattern mining. It focus on collective gathering problem on consecutive time period. Traditional models of gathering patterns are based on co-concurrence patterns. Mining methods based on such models generate a lot of stationary gathering groups. In order to deal with such problems, we propose a converging pattern based on modelling of group moving objects, which accurately identifies gathering group instead of other types of moving group. A moving objects converging pattern mining (CPM) algorithm is presented and implemented. First, the algorithm locates all high density peak points and converges central zones. Second, the algorithm identifies converging groups on consecutive timestamps, and then detects converging patterns according to the durability of group patterns. Experimental results show the effectiveness and efficiency of the algorithm.