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IP.LSH.DBSCAN: Integrated parallel density-based clustering by locality-sensitive hashing
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DOI：10.1016/j.dam.2025.11.047.png)
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Abstract
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Locality-sensitive hashing (LSH) is an established method for fast data indexing and approximate similarity search, with useful parallelism properties. Although indexes and similarity measures are key for data clustering, little has been investigated on the multifaceted benefits of LSH in the problem. We show how approximate DBSCAN clustering can be fused into the process of creating an LSH index, and, through parallelization and fine-grained synchronization, also utilize efficiently available computing capacity. The resulting algorithm, IP.LSH.DBSCAN, described in this article, can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse the algorithm's asymptotic completion time and provide an open-source prototype implementation. We also conduct a detailed evaluation measuring latency and accuracy metrics of IP.LSH.DBSCAN, on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. The analysis and the empirical study of IP.LSH.DBSCAN show how it complements the landscape of established state-of-the-art methods, by offering up to several orders of magnitude speed-up on higher dimensional datasets, with tunable high clustering accuracy. (c) 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Density-based clustering
Similarity-based clustering
Approximation algorithms
Data summarization
High-dimension data analytics
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