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DBSCAN Revisited, Revisited

open access: yesACM Transactions on Database Systems, 2017
At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN
Martin Ester, Erich Schubert, Xiaowei Xu
exaly   +2 more sources

A Fast Clustering Algorithm Based on Pruning Unnecessary Distance Computations in DBSCAN for High-Dimensional Data [PDF]

open access: yesPattern Recognition, 2018
Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding.
Cheng Wang, Hailin Li, Nizar Bouguila
exaly   +2 more sources
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BLOCK-DBSCAN: Fast Clustering for Large Scale Data

Pattern Recognition, 2021
Nizar Bouguila, Yewang Chen, Yi Chen
exaly  

ECR-DBSCAN: An Improved DBSCAN Based on Computational Geometry

Machine Learning With Applications, 2021
Kinsuk Giri
exaly  

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