An Efficient Method for Mining Spatial Co-location Patterns under Size Constraints

Spatial co-location pattern mining aims to discover subsets of spatial features that frequently occur in close geographic proximity. However, traditional methods usually discover patterns of all sizes, making them inefficient for mining patterns of a specific size specified by users. To address this issue, we define a problem called k-size Spatial Co-location Pattern (k-SCP) Mining, which focuses on discovering co-location patterns of a user-specified size k. Unlike the general size-wise SCP mining method, the k-SCP mining method directly targets patterns of specific sizes. Thus, it cannot utilize the downward closure property for candidate pruning. For this reason, we propose a novel k-SCP mining framework based on a representation model IC+(k-1)deg, called k-pattern-Miner. The IC+(k-1)deg integrates instance coloring with (k-1)-degree constraint mechanism, enabling the iterative elimination of instances and features that are irrelevant to k-SCPs, which substantially reduces the verification cost in subsequent mining steps. Extensive experiments on both real-world and synthetic datasets validate the efficiency of the k-pattern-Miner. We selected several state-of-the-art SCP mining methods to support k-SCP mining for comparison. Experimental results show that k-pattern-Miner improves the running time by several orders of magnitude compared to existing methods.