Parallel DBSCAN Algorithm Based on Cloud Computing Platform

Cai Yong-qiang; Chen Ping-hua; Li Hui

doi:10.3969/j.issn.1007-7162.2016.01.010

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Parallel DBSCAN Algorithm Based on Cloud Computing Platform

更新时间：2026-05-27

- Parallel DBSCAN Algorithm Based on Cloud Computing Platform
- Vol. 33, Issue 1, Pages: 51-56(2016)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2016.01.010
  CLC：
- Online First：16 January 2016，
  
  Published：2016
- 稿件说明：
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蔡永强, 陈平华, 李惠. 基于云计算平台的并行DBSCAN算法[J]. 广东工业大学学报, 2016, 33(1): 51-56.

Cai Yong-qiang, Chen Ping-hua, Li Hui. Parallel DBSCAN Algorithm Based on Cloud Computing Platform[J]. 2016, 33(1): 51-56.
蔡永强, 陈平华, 李惠. 基于云计算平台的并行DBSCAN算法[J]. 广东工业大学学报, 2016, 33(1): 51-56. DOI： 10.3969/j.issn.1007-7162.2016.01.010.

Cai Yong-qiang, Chen Ping-hua, Li Hui. Parallel DBSCAN Algorithm Based on Cloud Computing Platform[J]. 2016, 33(1): 51-56. DOI： 10.3969/j.issn.1007-7162.2016.01.010.

摘要

DBSCAN算法是一种典型的基于密度的聚类算法，具有速度快、可以发现噪声的优点，但在处理大规模数据时出现聚类效率低、内存和I/O消耗大、聚类精度降低的问题

集群式计算机技术特别是云计算技术的发展提供了解决DBSCAN算法缺陷的方案.文中提出了数据预分区的并行PMDBSCAN算法，该算法在聚类之前对数据分区预处理，利用并行编程模型MapReduce实现DBSCAN算法并行化，结合重叠分区思想，减少I/O消耗.实验结果表明，在大规模数据集上，PMDBSCAN算法聚类有效提高了聚类的速度、减少了I/O消耗、改善了聚类的质量.

Abstract

As a typical representative of clustering algorithm

DBSCAN algorithm has the advantages of fast speed and helps to find the noise of data. However

in big data processing

there are problems of low clustering efficiency

high memory and I/O requirement

and poor clustering precision. With the support of cluster computer technology especially the development of cloud computing

the solutions to the problems of DBSCAN algorithm mentioned above can be provided and progressed significantly. This paper proposes a parallel PMDBSCAN algorithm based on data partition which can pre-process data partition before clustering

realize parallelization of DBSCAN algorithm by parallel programming model MapReduce

and reduce I/O consumption according to overlapping partition. The results show that in dealing with large-scale data the PMDBSCAN algorithm increases the speed of clustering

reduces I/O consumption and improves cluster quality significantly.

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