A spatial co-location pattern refers to a subset of spatial features whose instances are Lighting Accessories frequently co-located in spatial.In spatial data mining, most of the existing algorithms aim to discover the positive patterns.Moreover, there are patterns with strong negative correlations in spatial, such as negative co-location patterns.
The discovery of such patterns is also greatly significant in some applications.Existing negative co-location patterns mining algori-thm is time-consuming and the number of mining results is huge.To address these problems, this paper explores the upward inclusion property of the negative co-location pattern, and proposes a minimal negative co-location pattern.
Based on the minimal negative co-location patterns, all prevalent negative co-location patterns can be derived.In the negative co-location pattern mining process, the calculation of table instances of the candidate patterns is the funda-mental factor that restricts the mining efficiency.In order to reduce the calculation of the table instance effectively, three pruning strategies are proposed.
A large number of experiments on real and synthetic data sets verify the correctness and efficiency of the proposed algorithm.In Hockey Skates - Junior - Elite particular, the experimental results show that the minimal negative co-location patterns can compress the number of prevalent negative co-location patterns by more than 80%.