FPGA-Based Parallel DBSCAN Architecture

Scicluna, Neil; Bouganis, Christos-Savvas

doi:10.1007/978-3-319-05960-0_1

Cited by 5 publications

(3 citation statements)

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“…The research presented in this paper extends the previous work in [Scicluna and Bouganis 2014]. We include more in depth coverage on the various research performed in this field, a more detailed background of the DBSCAN algorithm itself and more detailed explanations on the design choices made.…”

Section: Introductionmentioning

confidence: 91%

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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: Introductionmentioning

confidence: 91%

“…To the best of the authors' knowledge, there has been only one other FPGA implementation of DBSCAN developed since [Scicluna and Bouganis 2014]. This is the work proposed in [Shaobo Shi and Wang 2014], which also proposes an FPGA-based parallel architecture, but using a notably different parallelisation strategy.…”

Section: Related Workmentioning

confidence: 99%

Arc 2014

Scicluna

Bouganis

2015

ACM Trans. Reconfigurable Technol. Syst.

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“…Besides, in order to implement fast query of nearest neighbors, ISB-DBSCAN [21] was proposed to improve the traditional locality sensitive hashing approach. By utilizing graphics processing units (GPUs), CudaSCAN [24] is another method to boost efficiency of clustering from the hardware perspective [25]. In addition, some extended versions of DBSCAN with supporting of distributed and parallel computing [26]- [30] were implemented to apply for massive data analysis.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Cai

Wang

2020

IEEE Access

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Clustering is a classical research field due to its broad applications in data mining such as emotion detection, event extraction and topic discovery. It aims to discover intrinsic patterns which can be formed as clusters from a collection of data. Significant progress have been made by the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and its variants. However, there is a major limitation that current density-based algorithms suffer from linear connection problem, where they perform poorly to discriminate objective clusters which are ''connected'' by a few data points. Moreover, the parameter setting and the time cost make it hard to be well-adapted in massive data analysis. To address these problems, we propose a novel adaptive density-based spatial clustering algorithm called Ada-DBSCAN, which consists of a data block splitter and a data block merger, coordinated by local clustering and global clustering. We conduct extensive experiments on both artificial and real-world datasets to evaluate the effectiveness of Ada-DBSCAN. Experimental results show that our algorithm evidently outperforms several strong baselines in both clustering accuracy and human evaluation. Besides, Ada-DBSCAN shows significant improvement of efficiency compared with DBSCAN. INDEX TERMS Clustering, density-based algorithms, linear connection, data block splitter, data block merger.

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