PS-DBSCAN：基于AI（PAI）平台的高效并行DBSCAN算法

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by pr…

AMD-DBSCAN：一种面向密度变化极大数据集的自适应多密度DBSCAN

AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density

针对传统DBSCAN在多密度数据集上聚类效果不佳的问题，提出仅需一个超参数的自适应多密度DBSCAN算法AMD-DBSCAN，通过改进参数自适应方法搜索多组参数对，引入邻居数方差衡量簇间密度差，在降低复杂度、缩短运行时间的同时提升了多密度场景聚类准确率。

Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering

This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN$^*$). Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations. We introduce a new notion of well-separation to reduce the work and space of our algorithm for HDBSCAN$^*$. We also present a parallel approximate algorithm for OPTICS based on a recent sequential algorithm by Gan and Tao. Finally, we give a new parallel divide-and-conquer …