基于方形领域的网格密度聚类算法

o solve the problem of low efficiency of large data clustering, this paper proposes a fast grid density clustering algorithm SGBSCAN(Square-neighborhood and Grid-based DBSCAN) . Firstly, this paper gave the definition of square neighborhood density clustering , and used the square neighborhood instead of the circular neighborhood to reduce the time complexity. Secondly, this paper proposed the concept of grid of square neighborhood density clustering , to determine the density relationship between core points and data points in high density region quickly. Finally, this paper proposed the Grid density cluster, the method used the relationship between the grid to accelerate the formation of density clusters. The algorithm made 16 data sets and compared with the existing literature algorithms. The results shows that the algorithm has a significant improvement in clustering efficiency. The larger the data volume, the more obvious the efficiency of the algorithm, and the algorithm is suitable for multidimensional data clustering.