A Novel Approach for Effectively Mining of Spatially Co-Located Moving Objects from the Spatial Databases

In this paper, we have presented a novel approaches for effectively mining of spatially co-located moving objects from the spatial databases. We propose a novel technique 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 technique is efficient since it generates and filters the candidate instances. Subsequently, the neighborhood relationships are carried out by the designed neighborhood and the node membership functions which satisfy the minimum conditional threshold. This paper has been inspired by the Join-less approach for mining spatial co-location patterns. We use a spatial database that contains the moving objects and its corresponding spatial location for spatial co-location pattern mining to mine spatially co-located moving objects.