Mining frequent spatio-temporal sequential patterns
Mining frequent spatio-temporal sequential patterns
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DOI：
10.1109/icdm.2005.95
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
H. Cao;N. Mamoulis;D. Cheung
中科院分区：
文献类型：
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作者：
H. Cao;N. Mamoulis;D. Cheung
许多应用跟踪移动的对象的移动，其可以被表示为时间戳位置的序列。给定这样一个时空序列，我们研究发现顺序模式，这是经常遵循的对象的路线的问题。事务数据的序列模式挖掘算法不直接适用于此设置。要解决的挑战是：（i）在模式中的位置的模糊，和（ii）非明确的模式实例的识别。在本文中，我们定义的模式元素周围的频繁线段的空间区域。我们的方法首先将原始序列转化为序列片段列表，并以启发式的方式检测频繁区域。然后，我们提出了算法，发现模式，采用新提出的子串树结构和改进的先验技术。绩效评估证明了我们方法的有效性和效率。
Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatiotemporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this setting. The challenges to address are: (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving a priori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach.