In spatial data with complexity, different clusters can be very contiguous, and the density\nof each cluster can be arbitrary and uneven. In addition, background noise that does not belong to\nany clusters in the data, or chain noise that connects multiple clusters may be included. This makes it\ndifficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve\nthe nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in\ncomplex spatial data clustering, as well as to improve robustness against various types of noise in\nspatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that\nemploys Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of\napplications with noise). In the performance evaluation using simulated synthetic data as well as real\n3D point clouds, the proposed method maintained better clustering and separability of neighboring\nclusters compared to other clustering techniques, and is expected to be of practical use in the field of\nspatial data mining.
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