GriT-DBSCAN: A spatial clustering algorithm for very large databases

DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, bottleneck of its O(n2) worst-case time complexity. To address this limitation, we propose new grid-based for exact in Euclidean space called GriT-DBSCAN, which based on the following two techniques. First, introduce grid tree to organize non-empty grids purpose efficient neighboring queries. Second, by utilizing relationships among points, technique that iteratively prunes unnecessary distance calculations when determining whether minimum between sets less than or equal certain threshold. We theoretically demonstrate GriT-DBSCAN has excellent reliability terms In addition, obtain variants incorporating heuristics, combining second an existing algorithm. Experiments are conducted both synthetic and real-world data evaluate efficiency variants. The results show our algorithms outperform algorithms.