A Density Clustering Algorithm for Large-scale Two-dimensional Lattice Data

Aiming at the problem that the density clustering algorithm cannot be applied to large-scale data sets, a grid dividing-based density clustering algorithm (GDSCAN) was proposed. The large-scale two-dimensional lattice map was divided into several grids, the shortest edge of the grid was not less than the given neighborhood radius, then the neighborhood range of any point in the grid where the target point was located will not exceed the grid which directly conneced with the grid, only the neighborhood points need to be found in the reserved mesh, so as to reduce the calculation amount; Clustering started from any unclassified core point, and formed a cluster with all the density of the point, and so on until all core points had a class. The proposed GDSCAN algorithm was used to cluster the two-dimensional road network nodes of different orders of magnitude.The results show that the GDSCAN algorithm can effectively solve the efficiency problem of density clustering in large-scale two-dimensional lattice data sets, and the larger the amount of data is, the more obvious the effect is, and the time complexity is significantly reduced.