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 datasets. 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 identifying the table instances of co-location patterns. The essence of co-location patterns discovery and four
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----iCPI-tree (Improved Co-location Pattern Instance Tree), is proposed.
The iCPI-tree is an improved version of the CPI-tree which materializes spatial neighbor relationships in order to accelerate
the process of identifying co-location instances. This paper proves the correctness and completeness of the new approach.
Finally, an experimental evaluations using synthetic and real world datasets show that the algorithm is computationally