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.
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Titolo: | NG-DBSCAN: Scalable density-based clustering for arbitrary data | |
Autori: | ||
Data di pubblicazione: | 2016 | |
Rivista: | ||
Handle: | http://hdl.handle.net/11567/1071006 | |
Appare nelle tipologie: | 01.01 - Articolo su rivista |