多级空间尺度下的轨迹知识发现

Knowledge Discovery of Trajectories Under Multi-scale Environment

为了挖掘轨迹数据库在不同空间尺度上隐含的移动对象分布模式与运动规律,将空间语义信息融入轨迹的表达,运用空间层次关系模型结合多层关联规则挖掘方法,提出了一种基于语义位置矩阵的多尺度轨迹表达和演化方法,在此基础上设计了一种发现轨迹频繁模式的算法.试验结果表明,该方法能发现不同尺度的轨迹知识,并具有较高效率.

For the purpose of mining distributions and movement patterns of moving objects at different levels of details,this paper presents a semantic-based multi-scale trajectory modeling and knowledge discovery method.Two matrixes are used to represent the multi-scale trajectory.A matrix based frequent item-set mining algorithm is developed to discover the association-rules in trajectory database.Experiments show that the approach is correct and feasible.