GriT-DBSCAN：一种面向超大型数据库的空间聚类算法
GriT-DBSCAN: A Spatial Clustering Algorithm for Very Large Databases
摘要：
DBSCAN是一种基本的空间聚类算法，具有许多实际应用。然而，该算法的一个瓶颈是在最坏的情况下，运行时间复杂度为$O(n^2)$。为了解决这个问题，我们提出了一个新的基于网格的算法，精确DBSCAN在欧几里得空间称为GriT-DBSCAN，这是基于以下两种技术。首先，我们引入了一个网格树来组织非空网格的目的，有效的非空相邻网格查询。其次，通过利用点之间的空间关系，我们提出了一种技术，迭代修剪不必要的距离计算时，确定两组之间的最小距离是否小于或等于一定的阈值。我们从理论上证明了GriT-DBSCAN的复杂度与数据集大小成线性关系。此外，我们得到两个变种的GriT-DBSCAN通过将philistics，或通过将第二种技术与现有的算法相结合。在合成数据集和真实数据集上进行实验，以评估GriT-DBSCAN及其变体的效率。我们的分析结果表明，我们的算法优于现有的算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
