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Co-location pattern discovery is intended towards the processing data with spatial contexts to discover classes of spatial objects that are frequently located together. Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors in space. In this paper, the authors have presented an algorithm for mining spatially co-located moving objects using spatial data mining techniques. they propose a novel algorithm for co-location pattern mining which materializes spatial neighbor relationships with no loss of co-location instances and reduces the computational cost with the aid of the Prim's Algorithm. The spatially co-location mining algorithm is proficient since it generates and filters the candidate instances.
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