摘要：
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
more efficient.
