IP. LSH. DBSCAN : Integrated Parallel Density-Based Clustering Through Locality-Sensitive Hashing
Paper i proceeding, 2022

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 benefits of LSH in the problem. Our proposition is that LSH can be extremely beneficial for parallelizing high-dimensional density-based clustering e.g., DBSCAN, a versatile method able to detect clusters of different shapes and sizes. We contribute to fill the gap between the advancements in LSH and density-based clustering. 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 method, IP. LSH. DBSCAN, can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse its properties and evaluate our prototype implementation on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. Our results show that IP. LSH. DBSCAN effectively complements established state-of-the-art methods by up to several orders of magnitude of speed-up on higher dimensional datasets, with tunable high clustering accuracy through LSH parameters.


Amir Keramatian

Nätverk och System

Vincenzo Massimiliano Gulisano

Nätverk och System

Marina Papatriantafilou

Nätverk och System

Philippas Tsigas

Nätverk och System

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13440 LNCS 268-284
9783031125966 (ISBN)

28th International European Conference on Parallel and Distributed Computing, Euro-Par 2022
Glasgow, United Kingdom,

Molnbaserade produkter och produktion (FiC)

Stiftelsen för Strategisk forskning (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.


Informations- och kommunikationsteknik




Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Datavetenskap (datalogi)



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