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A Grid and Density Based Fast Spatial Clustering Algorithm

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2 Author(s)
Ming Huang ; Res. Center of Spatial Inf. & Digital Eng., Wuhan Univ., Wuhan, China ; Fuling Bian

Density-based spatial clustering algorithm DBSCAN has a relatively low efficiency since it carries out a large number of useless distance computing; Grid-based spatial clustering algorithm is more efficient, but the clustering result has a low accuracy. Considering the advantage and disadvantages of the two algorithms, this paper proposes a grid and density based fast clustering algorithm GNDBSCAN. This algorithm performs density-based clustering on datasets space, which has been divided by grids. It improves the efficiency of clustering and at the same time, maintains high accuracy for clustering results.

Published in:

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on  (Volume:4 )

Date of Conference:

7-8 Nov. 2009