Mining co-location patterns from spatial databases may disclose the types of spatial features which ar e likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spat ially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spa tial co-location patterns took much computational c ost. In order to reduce the computation time, in this st udy, we make use of R-tree that is spatial data str ucture to mine the spatial co-location patterns. The importan t step presented in the approach is that the transf ormation of spatial data into the compact format that is wel l-suitable to mine the patterns. Here, we have adap ted the R-tree structure that converts the spatial data wit h the feature into the transactional data format. T hen, the prominent pattern mining algorithm, FP growth is us ed to mine the spatial co-location patterns from th e converted format of data. Finally, the performance of the proposed technique is compared with the prev ious technique in terms of time and memory usage. From the results, we can ensure that the proposed techniq ue outperformed of about more than 50% of previous algorithm in time and memory usage.
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