Wang, Lizhen, Bao, Yuzhen, Lu, Joan and Yip, Yau Jim (2008) A new join-less approach for co-location pattern mining. In: 2008 8th IEEE International Conference on Computer and Information Technology. IEEE, pp. 197-202. ISBN 9781424423576

With the rapid growth and extensive applications of the spatial dataset, itpsilas getting more important to solve how to find spatial knowledge automatically from spatial datasets. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Itpsilas 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 co-location 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 datasets and a real world dataset shows that the algorithm is computationally more efficient than the join-less algorithm

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