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 NGDBSCAN: Scalable DensityBased Clustering for Arbitrary Data |
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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. |
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