Download details
NGDBSCAN: Scalable DensityBased Clustering for Arbitrary Data NGDBSCAN: Scalable DensityBased Clustering for Arbitrary Data HOT

Alessandro Lulli, Matteo Dell’Amico, Pietro Michiardi, Laura Ricci

Proceedings of the VLDB Endowment (VLDB '16)

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.

Data

Version
Size1.08 MB
Downloads176.00
Language
License
Author
Website
Price
Created2017-01-30
Created bySuper User
Changed
Changed by

Download

You are here: Home Publications NGDBSCAN: Scalable DensityBased Clustering for Arbitrary Data