Paper Title:
Research on Parallel DBSCAN Algorithm Design Based on MapReduce
  Abstract

Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, more researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel DBSCAN clustering algorithm based on Hadoop, which is a simple yet powerful parallel programming platform. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.

  Info
Periodical
Advanced Materials Research (Volumes 301-303)
Chapter
Chapter 2: Measuring and Testing Techniques
Edited by
Riza Esa and Yanwen Wu
Pages
1133-1138
DOI
10.4028/www.scientific.net/AMR.301-303.1133
Citation
Y. X. Fu, W. Z. Zhao, H. F. Ma, "Research on Parallel DBSCAN Algorithm Design Based on MapReduce", Advanced Materials Research, Vols. 301-303, pp. 1133-1138, 2011
Online since
July 2011
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Price
$32.00
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