Mining Significant Co-location Patterns From Spatial Regional Objects

A co-location pattern refers to the subset of features which frequently appear together in spatial proximity. There are many literatures studied the approach of discovering co-location patterns. However, a lot of proposed approaches need some thresholds given by the user, and it is difficult to give the proper thresholds. Moreover, most proposed approaches treat the spatial object as a point during the mining process, but spatial objects are dynamic or appear in the form of a cluster normally, which means that their locations are polygons rather than points. This paper provides a novel framework to mine co-location patterns from spatial regional objects. At first, we redefine the interest measure of significant co-locations. In our framework, the user does not need to specify any threshold, and the redefined interest measure is monotonically non-increasing which can be used for improving the mining efficiency. Then, an algorithm based on the grid partition is proposed to reduce time complexity further. Finally, we verify the efficiency and effectiveness of the proposed approach by extensive experiments.