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- W4384661760 abstract "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 give a new parallel divide-and-conquer algorithm for computing the dendrogram and reachability plots, which are used in visualizing clusters of different scale that arise for both EMST and HDBSCAN^*. We show that our algorithms are theoretically efficient: they have work (number of operations) matching their sequential counterparts, and polylogarithmic depth (parallel time). We implement our algorithms and propose a memory optimization that requires only a subset of well-separated pairs to be computed and materialized, leading to savings in both space (up to 10x) and time (up to 8x). Our experiments on large real-world and synthetic data sets using a 48-core machine show that our fastest algorithms outperform the best serial algorithms for the problems by 11.13--55.89x, and existing parallel algorithms by at least an order of magnitude." @default.
- W4384661760 created "2023-07-20" @default.
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- W4384661760 date "2023-07-18" @default.
- W4384661760 modified "2023-09-23" @default.
- W4384661760 title "Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering (Abstract)" @default.
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- W4384661760 doi "https://doi.org/10.1145/3597635.3598025" @default.
- W4384661760 hasPublicationYear "2023" @default.
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