Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Xiaogang Huang, Tiefeng Ma, Conan Liu, Shuangzhe Liu

Publisher name:  ScienceDirect

DOI:  10.1016/j.patcog.2023.109658

Volume Information:  Volume 142,(2023)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S003132032300359X