Publication Date:
2017-12-13
Description:
Remote Sensing, Vol. 9, Pages 1301: Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform Remote Sensing doi: 10.3390/rs9121301 Authors: Fang Huang Qiang Zhu Ji Zhou Jian Tao Xiaocheng Zhou Du Jin Xicheng Tan Lizhe Wang Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering algorithm that has the characteristics of being able to discover clusters of any shape, effectively distinguishing noise points and naturally supporting spatial databases. DBSCAN has been widely used in the field of spatial data mining. This paper studies the parallelization design and realization of the DBSCAN algorithm based on the Spark platform, and solves the following problems that arise when computing macro data: the requirement of a great deal of calculation using the single-node algorithm; the low level of resource-utilization with the multi-node algorithm; the large time consumption; and the lack of instantaneity. The experimental results indicate that the proposed parallel algorithm design is able to achieve more stable speedup at an increased involved spatial data scale.
Electronic ISSN:
2072-4292
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
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