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中文摘要：
针对不确定移动轨迹ε-邻域的空间分布特征,提出一种基于网格分割面积的不确定轨迹近邻网格概率匹配方法,将原始不确定移动轨迹数据转换为以网格单元表示的概率序列数据,通过对经典序列模式挖掘算法Prefix Span的相关改进,设计并实现了适应于严格时间间隔约束条件下的移动概率序列模式挖掘算法UTFP-Prefix Span.合成数据的测试实验仿真结果表明,本文所提出的方法较基于距离的概率转换方法在挖掘结果、可扩展性等方面具有更好的性能.
英文摘要：
For the e-neighborhood spatial distribution characteristics of uncertain trajectory data, we propose a neighboring grid probability matching approach based on segmentation area of adjacent grid cells. Thus uncertain original trajectory data can be translated into probability sequence data with spatial grid ceils. By modified the classical PrefixSpan algorithm, we devise a novel algorithm named UTFP-PrefixSpan to mine frequent moving trajectory pattern with strict time interval constraints. The experiment results from synthetic datasets show that the proposed method has better performance on mining result, scalability than existing method.
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