Abstract
In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.
Highlights
Spatial data mining, which intelligently extracts implicit and useful information from a large amount of spatial data, and considers spatial autocorrelation and heterogeneity, has become increasingly necessary as the size of such data and the associated complexity increases [1].After analyzing data without prior knowledge, such as the probability distribution or the number of clusters, classification into similar groups is a key aspect of spatial data mining
To evaluate the performance of the proposed DTSCAN method, three sets of virtual twotwo-dimensional spatial data and three-dimensional point clouds collected from a LiDAR mounted dimensional spatial data and three-dimensional point clouds collected from a LiDAR mounted on an on an actual terrestrial vehicle were utilized [31,32,33]
Two-dimensional spatial data were selected as a data a data set including uneven density characteristics, various cluster shapes, and noise, and the images set including uneven density characteristics, various cluster shapes, and noise, and the images included in the KITTI benchmark-object detection were used as point clouds, where the pedestrians included in the KITTI benchmark-object detection were used as point clouds, where the pedestrians assigned to the class were selected on an object-by-object basis [34]
Summary
Spatial data mining, which intelligently extracts implicit and useful information from a large amount of spatial data, and considers spatial autocorrelation and heterogeneity, has become increasingly necessary as the size of such data and the associated complexity increases [1].After analyzing data without prior knowledge, such as the probability distribution or the number of clusters, classification into similar groups is a key aspect of spatial data mining. Among them are density-based clustering [4,5,6,7], which calculates density according to neighboring data, and graph-based clustering [8,9,10], which extracts corresponding points using a mathematical model through an affinity matrix. These techniques show excellent performance in detecting clusters with uniform data distribution and ideal shapes, and clusters with some nonlinearity, and have been applied to various fields such as traffic
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