A New Join-less Approach for Co-location Pattern Mining

With the rapid growth and extensive applications of the spatial dataset, it's getting more important to solve how to find spatial knowledge automatically from spatial dataseis. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It's difficult to discovery co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating the table instances of colocation patterns. The essence of co-location patterns discovery and three kinds of co-location patterns mining algorithms proposed in recent years are analyzed, and a new join-less approach for co-location patterns mining, which based on a data structure--CPI-tree (Co-location Pattern Instance Tree), is proposed. The CPI-tree materializes spatial neighbor relationships. All co-location table instances can be generated quickly with a CPI-tree. This paper proves the correctness and completeness of the new approach. Finally, an experimental evaluation using synthetic dataseis and a real world dataset shows that the algorithm is computationally more efficient than the join-less algorithm.