G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering

Andrade, Guilherme; Ramos, Grasieli de Oliveira; Madeira, Daniel; Sachetto, Rafael; Ferreira, Renato; Rocha, Leonardo

doi:10.1016/j.procs.2013.05.200

Cited by 118 publications

(40 citation statements)

References 11 publications

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“…In conclusion, when compared to established software methods, simulations show that the proposed design achieves considerable performance benefits, which are higher than those obtained using the GPU implementations in [Thapa et al 2010] and [Andrade et al 2013]. Additionally, performance gains are achieved even with small datasets, as there is very little overhead with the proposed parallelisation strategy.…”

Section: Future Work and Conclusionmentioning

confidence: 85%

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Arc 2014

Scicluna

Bouganis

2015

ACM Trans. Reconfigurable Technol. Syst.

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Clustering large numbers of data points is a very computationally demanding task that often needs to be accelerated in order to be useful in practical applications. This work focuses on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is one of the state-of-the-art clustering algorithms, targeting its acceleration using an FPGA device. The paper presents an, optimised, scalable and parameterisable architecture that takes advantage of the internal memory structure of modern FPGAs in order to deliver a high performance clustering system. Post-synthesis simulation results show that the developed system can obtain mean speed-ups of 31x in real-world tests and 202x in synthetic tests when compared to state-of-the-art software counterparts running on a quad-core 3.4 GHz Intel i7-2600k. Additionally, this implementation is also capable of clustering data with any number of dimensions without impacting the performance.

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Section: Future Work and Conclusionmentioning

confidence: 85%

“…The second approach involves computing the range queries of all the points in parallel and once again storing the results in memory. A different approach is proposed in [Andrade et al 2013] called G-DBSCAN. This system manages to extract a very significant amount of parallelism by indexing the data using graphs.…”

Section: Related Workmentioning

confidence: 99%

Arc 2014

Scicluna

Bouganis

2015

ACM Trans. Reconfigurable Technol. Syst.

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“…G-DBSCAN [35] is a GPU accelerated parallel algorithm for density-based clustering algorithm, DBSCAN. It is one of the recently proposed algorithms in this category.…”

Section: Gpu Based Parallel Clusteringmentioning

confidence: 99%

Big Data Clustering: A Review

Shirkhorshidi

Aghabozorgi

Wah

et al. 2014

Lecture Notes in Computer Science

178

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Abstract.Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today's novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today's available technologies and frameworks.

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“…= Unclassified then (7) if . = Unclassified then (8) Create c-cluster ← ( ); (9) if expandcluster ( , , , , , ) then (10) Create c-cluster ← ( ++); (11) end if (12) end if (13) the cells according to the received points and given in each of the mappers, so the cell with the same in different mappers stands for the same area; thus we can only use the cell to locate the assigned range in overall data space. (2) The points in an inclusive cell must belong to a c-cluster, so the cell and c-cluster are enough to stand for classification of all points in the cell.…”

Section: Cludoop Frameworkmentioning

confidence: 99%

Cludoop: An Efficient Distributed Density-Based Clustering for Big Data Using Hadoop

Zhao

Wang

et al. 2015

International Journal of Distributed Sensor Networks

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Density-based clustering for big data is critical for many modern applications ranging from Internet data processing to massivescale moving object management. This paper proposes Cludoop algorithm, an efficient distributed density-based clustering for big data using Hadoop. First, we propose a serial clustering algorithm CluC by leveraging cell partition optimization and c-cluster to fast find clusters. CluC completes classification of the points using the relationships of connected cells around points instead of expensive completed neighbor query, which significantly reduce the number of distance calculations. Second, we propose the Cludoop, which can efficiently cluster very-large-scale data in parallel using already existing data partition on Map/Reduce platform. It employs the proposed serial clustering CluC as a plugged-in clustering on parallel mapper, along with a cell description instead of completed cell in transmission to reduce both network and I/O costs. Guided by proposed cell-based principles, we also design a Merging-Refinement-Merging 3-step framework to merge c-clusters on the overlay of assigned preclustering result on reducer. Finally, our comprehensive experimental evaluation on 10 network-connected commercial PCs, using both huge-volume real and synthetic data, demonstrates (1) the effectiveness of our algorithm in finding correct clusters with arbitrary shape and (2) the fact that our proposed algorithm exhibits better scalability and efficiency than state-of-the-art method.

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G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering

Cited by 118 publications

References 11 publications

Arc 2014

Arc 2014

Big Data Clustering: A Review

Cludoop: An Efficient Distributed Density-Based Clustering for Big Data Using Hadoop

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