Incremental Mining Method of Spatial Co-Location Pattern based on Genetic Algorithm

Abstract

In the traditional incremental mining under the spatial co-location mode, parallel computing is often used for obstacle clustering, which causes the running time of the mining method to be greatly affected by the conditional parameters. Therefore, an incremental mining method of spatial co-location pattern based on genetic algorithm is proposed. Obstacles are divided by Euclidean distance and thresholds are set, genetic algorithms are used to cluster the obstacles, the fitness function of the obstacles is established, and the frequent clustering increment method is used to increase the clustering set. Based on the IULEP clustering set incremental mining algorithm, the calculation is performed to complete the incremental mining. In order to verify the effectiveness of the designed method, a simulation experiment is designed, and the experimental results prove that the running time of the designed incremental mining method is less affected by the conditional parameters, and the mining time is shorter, which is feasible.