GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing - Deng, Chao; Song, Jinwei; Sun, Ruizhi; Cai, Saihua; Shi, Yinxue (Pattern recognition letters, 2018)
•Propose a new effective density-based and grid-based clustering algorithm GRIDEN for massive spatial data.•Present a new concept of ε-neighbor cells to improve the clustering accuracy of grid-based algorithm.•Present a parallel computing algorithm for high dimensional density-based clustering to achieve high performance.•Supports for multi-density clustering and incremental density-based clustering. Density-based clustering has been widely used in many fields. A new effective grid-based and density-based spatial clustering algorithm, GRIDEN, is proposed in this paper, which supports parallel computing in addition to multi-density clustering. It constructs grids using hyper-square cells and provides users with parameter k to control the balance between efficiency and accuracy to increase the flexibility of the algorithm. Compared with conventional density-based algorithms, it achieves much higher performance by eliminating distance calculations among points based on the newly proposed concept of ε-neighbor cells. Compared with conventional grid-based algorithms, it uses a set of symmetric (2k+1)D cells to identify dense cells and the density-connected relationships among cells. Therefore, the maximum calculated deviation of ε-neighbor points in the grid-based algorithm can be controlled to an acceptable level through parameter k. In our experiments, the results demonstrate that GRIDEN can achieve a reliable clustering result that is infinite closed with respect to the exact DBSCAN as parameter k grows, and it requires computational time that is only linear to N.