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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20141119T200000Z
DTEND:20141119T203000Z
LOCATION:388-89-90
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: DBSCAN is a widely used isodensity-based clustering algorithm for particle-data well-known for its ability to isolate arbitrarily-shaped clusters and to filter noise-data. The algorithm is super-linear  (O(nlogn)) and computationally expensive for large-datasets. Given the need for speed, we propose an approximate DBSCAN algorithm using density-based-sampling, which performs equally well in quality compared to exact algorithms, but is more than an order-of-magnitude faster. Our experiments on  astrophysics and synthetic massive-datasets (8.5B numbers) shows that our approximate algorithm is upto 56x faster than exact algorithms with almost identical quality (Omega-Index>=0.99). We  develop a new parallel DBSCAN algorithm, which uses dynamic-partitioning  to improve load-balancing and locality. We demonstrate near-linear speedup on shared memory (15x on 16-core Intel® Xeon® E5-2680 systems and 59x on Intel® Xeon Phi™ with 2x performance improvement over Xeon) and distributed memory (3917x using 4096 Xeon cores) computers. Additionally, existing exact algorithms can achieve upto 3.4 times speedup using dynamic-partitioning.
SUMMARY:Pardicle: Parallel Approximate Density-Based Clustering
PRIORITY:3
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