Pardicle parallel approximate density-based clustering

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...

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Vydáno v:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis s. 560 - 571
Hlavní autoři: Patwary, Md. Mostofa Ali, Satish, Nadathur, Sundaram, Narayanan, Manne, Fredrik, Habib, Salman, Dubey, Pradeep
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Piscataway, NJ, USA IEEE Press 16.11.2014
IEEE
Edice:ACM Conferences
Témata:
approximate clustering algorithm
Approximation algorithms
Approximation methods
Clustering algorithms
Computer systems organization > Dependable and fault-tolerant systems and networks
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis
Data structures
Density based clustering
Disjoint-set data structure
General and reference > Cross-computing tools and techniques > Performance
Heuristic algorithms
Instruction sets
Mathematics of computing > Mathematical analysis > Functional analysis > Approximation
Networks > Network performance evaluation
Partitioning algorithms
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis
Union-Find algorithm
approximate clustering algorithm
union-find algorithm
disjoint-set data structure
density based clustering
ISBN:1479955000, 9781479955008
ISSN:2167-4329
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Popis
Shrnutí: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 a fast heuristic algorithm for Dbscan 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 (8.5 billion numbers) shows that our approximate algorithm is up to 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 using 16 cores, single node Intel® Xeon® processor) and distributed memory (3917x using 4096 cores, multinode) computers, with 2x additional performance improvement using Intel® Xeon Phi™ coprocessors. Additionally, existing exact algorithms can achieve up to 3.4 times speedup using dynamic partitioning.
ISBN:1479955000
9781479955008
ISSN:2167-4329
DOI:10.1109/SC.2014.51