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

• Published in: Pattern recognition Vol. 142; p. 109658
• Main Authors: Huang, Xiaogang, Ma, Tiefeng, Liu, Conan, Liu, Shuangzhe
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Clustering; DBSCAN; Indexing methods; Spatial databases; DBSCAN; Indexing methods; Spatial databases; Clustering
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.109658

•A grid-based algorithm for exact DBSCAN is proposed for large databases.•Grid tree is devised to speed up non-empty neighboring grids queries.•Use the spatial relationships among points to omit unnecessary distance calculations.•The efficiency of the proposed algorithm is proved theoretically and experimentally. DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, a bottleneck of DBSCAN is its O(n2) worst-case time complexity. 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 grid tree to organize the non-empty grids for the purpose of efficient non-empty neighboring grids queries. Second, by utilizing 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 demonstrate that GriT-DBSCAN has excellent reliability in terms of time complexity. 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 show that our algorithms outperform existing algorithms.