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Καλησπέρα σας, <br>
<br>
Την Παρασκευή 16/03 θα παρουσιάσω το paper με τίτλο <br>
<br>
<b>NG-DBSCAN: Scalable Density-Based Clustering for
Arbitrary Data<br>
<br>
</b>των:<br>
<br>
Alessandro Lulli
, Matteo Dell’Amico
, Pietro Michiardi
, Laura Ricci<br>
<br>
το οποίο δημοσιεύτηκε στο vldb του 2016. Το abstract του paper:<br>
<br>
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.<br>
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