Graduate School and Research Center in Digital Sciences

NG-DBSCAN: Scalable density-based clustering for arbitrary data

Lulli, Alessandro; Dell?Amico, Matteo; Michiardi, Pietro; Ricci, Laura

VLDB 2016, 42nd International Conference on Very Large Data Bases, September 5-9, 2016, New-Delhi, India / Proceedings of the VLDB Endowment, 2016, Vol.10, N°3

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.

Document Bibtex

Title:NG-DBSCAN: Scalable density-based clustering for arbitrary data
Department:Data Science
Eurecom ref:5076
Copyright: VLDB
Bibtex: @inproceedings{EURECOM+5076, year = {2016}, title = {{NG}-{DBSCAN}: {S}calable density-based clustering for arbitrary data}, author = {{L}ulli, {A}lessandro and {D}ell?{A}mico, {M}atteo and {M}ichiardi, {P}ietro and {R}icci, {L}aura}, booktitle = {{VLDB} 2016, 42nd {I}nternational {C}onference on {V}ery {L}arge {D}ata {B}ases, {S}eptember 5-9, 2016, {N}ew-{D}elhi, {I}ndia / {P}roceedings of the {VLDB} {E}ndowment, 2016, {V}ol.10, {N}°3 }, address = {{N}ew-{D}elhi, {INDIA}}, month = {09}, url = {} }
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