Istituto di Scienza e Tecnologie dell'Informazione     
Lulli A., Dell'Amico M., Michiardi P., Ricci L. NGDBSCAN: scalable density based clustering for arbitrary data. In: Proceedings of the VLDB Endowment, vol. 10 (3) pp. 157 - 168. VLDB Endowment Inc, 2016.
We present NG-DBSCAN, an approximate density-based clustering algorithm that can operate with arbitrary similarity metrics. 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 various steps of NG-DBSCAN, together with their analysis. Our results, which we obtain through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.
URL: http://www.vldb.org/pvldb/vol10.html
Subject Clustering
H.2.8 DATABASE MANAGEMENT. Database Applications. Data Mining

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