NG-DBSCAN: scalable density-based clustering for arbitrary data. Lulli, A., Dell'Amico, M., Michiardi, P., & Ricci, L. Proceedings of the VLDB Endowment, 10(3):157–168, November, 2016. ![NG-DBSCAN: scalable density-based clustering for arbitrary data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.
@article{lulli_ng-dbscan_2016,
title = {{NG}-{DBSCAN}: scalable density-based clustering for arbitrary data},
volume = {10},
issn = {2150-8097},
shorttitle = {{NG}-{DBSCAN}},
url = {https://doi.org/10.14778/3021924.3021932},
doi = {10.14778/3021924.3021932},
abstract = {We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.},
number = {3},
urldate = {2021-10-03},
journal = {Proceedings of the VLDB Endowment},
author = {Lulli, Alessandro and Dell'Amico, Matteo and Michiardi, Pietro and Ricci, Laura},
month = nov,
year = {2016},
pages = {157--168},
}
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