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首页> 外文期刊>American journal of applied sciences >AN EFFICIENT ALGORITHM FOR MINING SPATIALLY CO-LOCATED MOVING OBJECTS | Science Publications
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AN EFFICIENT ALGORITHM FOR MINING SPATIALLY CO-LOCATED MOVING OBJECTS | Science Publications

机译：一种有效的挖掘空间错位移动对象的算法|科学出版物

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摘要

> Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors? in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim?s Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took much computational cost. In order to reduce the computation time, in this study, we make use of R-tree that is spatial data structure to mine the spatial co-location patterns. The important step presented in the approach is that the transformation of spatial data into the compact format that is well-suitable to mine the patterns. Here, we have adapted the R-tree structure that converts the spatial data with the feature into the transactional data format. Then, the prominent pattern mining algorithm, FP growth is used to mine the spatial co-location patterns from the converted format of data. Finally, the performance of the proposed technique is compared with the previous technique in terms of time and memory usage. From the results, we can ensure that the proposed technique outperformed of about more than 50% of previous algorithm in time and memory usage.
机译： >从空间数据库中挖掘同位模式可能会揭示可能定位为邻居的空间特征的类型？在太空。因此，我们提出了一种先前使用空间数据挖掘技术和Prim？s算法来挖掘在空间上位于同一位置的运动对象的算法。在以前的技术中，扫描数据库以挖掘空间共址模式会花费大量计算成本。为了减少计算时间，在本研究中，我们利用作为空间数据结构的R树来挖掘空间共置模式。该方法中提出的重要步骤是将空间数据转换为非常适合挖掘模式的紧凑格式。在这里，我们调整了R树结构，将具有特征的空间数据转换为事务数据格式。然后，使用著名的模式挖掘算法FP growth从转换后的数据格式中挖掘空间共址模式。最后，在时间和内存使用方面，将所提出技术的性能与先前技术进行比较。从结果中，我们可以确保所提出的技术在时间和内存使用方面的性能优于先前算法的50％以上。

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