期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2012
卷号：3
期号：2
页码：344-348
出版社：Engg Journals Publications
摘要：Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbours in space. Accordingly, we presented 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 work, 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. According to, 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.
关键词：Spatial data mining; Co-location patterns; minimum support; minimum bounding rectangle; FP tree; Vehicle movement data.
