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arXiv · 2210.07580

GriT-DBSCAN: A Spatial Clustering Algorithm for Very Large Databases

Xiaogang Huang·Tiefeng Ma·Conan Liu·Shuangzhe Liu
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Abstract

DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, a bottleneck of the algorithm is in the worst case, the run time complexity is $O(n^2)$. To address this limitation, we propose a new grid-based algorithm for exact DBSCAN in Euclidean space called GriT-DBSCAN, which is based on the following two techniques. First, we introduce a grid tree to organize the non-empty grids for the purpose of efficient non-empty neighboring grids queries. Second, by utilising the spatial relationships among points, we propose a technique that iteratively prunes unnecessary distance calculations when determining whether the minimum distance between two sets is less than or equal to a certain threshold. We theoretically prove that the complexity of GriT-DBSCAN is linear to the data set size. In addition, we obtain two variants of GriT-DBSCAN by incorporating heuristics, or by combining the second technique with an existing algorithm. Experiments are conducted on both synthetic and real-world data sets to evaluate the efficiency of GriT-DBSCAN and its variants. The results of our analyses show that our algorithms outperform existing algorithms.

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Xiaogang Huang, Tiefeng Ma, Conan Liu, Shuangzhe Liu. 2022-11-06. GriT-DBSCAN: A Spatial Clustering Algorithm for Very Large Databases. https://arxiv.org/abs/2210.07580

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