Partial spatio-temporal co-occurrence pattern mining
Partial spatio-temporal co-occurrence pattern mining
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DOI：
10.1007/s10115-014-0750-2
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发表时间：
2015-07
影响因子：
2.7
通讯作者：
Mete Celik
中科院分区：
文献类型：
--
作者：
Mete Celik
时空同现模式表示通常在空间和时间上位于一起的对象类型的子集。部分时空同现模式发现（PACOP）的目的是发现数据库中部分存在的对象类型的同现。发现PACOPs是许多应用的重要问题，例如发现生态学中动物之间的相互作用，识别战场和游戏中的战术，以及识别犯罪数据库中的犯罪模式。然而，挖掘PACOP在计算上是非常昂贵的，因为兴趣度量在计算上是复杂的，数据库由于存档历史而更大，并且候选模式的集合在对象类型的数量上是指数的。先前关于发现时空共现模式的研究没有考虑存在时段（即，数据库中的对象的寿命）。本文定义了PACOP挖掘问题，提出了一种新的单调综合兴趣测度，并提出了新的PACOP挖掘算法。实验结果表明，所提出的算法是更有效的计算比天真的替代品。
Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal co-occurrence patterns (PACOPs) is to find co-occurrences of the object-types that are partially present in the database. Discovering PACOPs is an important problem with many applications such as discovering interactions between animals in ecology, identifying tactics in battlefields and games, and identifying crime patterns in criminal databases. However, mining PACOPs is computationally very expensive because the interest measures are computationally complex, databases are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. Previous studies on discovering spatio-temporal co-occurrence patterns do not take into account the presence period (i.e., lifetime) of the objects in the database. This paper defines the problem of mining PACOPs, proposes a new monotonic composite interest measure, and proposes novel PACOP mining algorithms. The experimental results show that the proposed algorithms are computationally more efficient than the naïve alternatives.