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},
}
Downloads: 0
{"_id":"9AuY7Gi7e7iBJsTLs","bibbaseid":"lulli-dellamico-michiardi-ricci-ngdbscanscalabledensitybasedclusteringforarbitrarydata-2016","author_short":["Lulli, A.","Dell'Amico, M.","Michiardi, P.","Ricci, L."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Lulli"],"firstnames":["Alessandro"],"suffixes":[]},{"propositions":[],"lastnames":["Dell'Amico"],"firstnames":["Matteo"],"suffixes":[]},{"propositions":[],"lastnames":["Michiardi"],"firstnames":["Pietro"],"suffixes":[]},{"propositions":[],"lastnames":["Ricci"],"firstnames":["Laura"],"suffixes":[]}],"month":"November","year":"2016","pages":"157–168","bibtex":"@article{lulli_ng-dbscan_2016,\n\ttitle = {{NG}-{DBSCAN}: scalable density-based clustering for arbitrary data},\n\tvolume = {10},\n\tissn = {2150-8097},\n\tshorttitle = {{NG}-{DBSCAN}},\n\turl = {https://doi.org/10.14778/3021924.3021932},\n\tdoi = {10.14778/3021924.3021932},\n\tabstract = {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.},\n\tnumber = {3},\n\turldate = {2021-10-03},\n\tjournal = {Proceedings of the VLDB Endowment},\n\tauthor = {Lulli, Alessandro and Dell'Amico, Matteo and Michiardi, Pietro and Ricci, Laura},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {157--168},\n}\n\n","author_short":["Lulli, A.","Dell'Amico, M.","Michiardi, P.","Ricci, L."],"key":"lulli_ng-dbscan_2016","id":"lulli_ng-dbscan_2016","bibbaseid":"lulli-dellamico-michiardi-ricci-ngdbscanscalabledensitybasedclusteringforarbitrarydata-2016","role":"author","urls":{"Paper":"https://doi.org/10.14778/3021924.3021932"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":[],"search_terms":["dbscan","scalable","density","based","clustering","arbitrary","data","lulli","dell'amico","michiardi","ricci"],"title":"NG-DBSCAN: scalable density-based clustering for arbitrary data","year":2016}