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DGCL: An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database

  • Ho Seok Kim
  • Song Gao
  • Ying Xia
  • Gyoung Bae Kim
  • Hae Young Bae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)

Abstract

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set makes the clustering process extremely costly. In this paper, we propose DGCL, an enhanced Density-Grid based Clustering algorithm for Large spatial database. The characteristics of dense area can be enhanced by considering the affection of the surrounding area. Dense areas are analytically identified as clusters by removing sparse area or outliers with the help of a density threshold. Synthetic datasets are used for testing and the result shows the superiority of our approach.

Keywords

Cluster Algorithm Spatial Data Spatial Database Base Cluster Algorithm Spatial Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ho Seok Kim
    • 1
  • Song Gao
    • 2
  • Ying Xia
    • 2
  • Gyoung Bae Kim
    • 3
  • Hae Young Bae
    • 1
  1. 1.Department of Computer Science and Information EngineeringInha UniversityIncheonKorea
  2. 2.College of Computer Science and TechnologyChongqing University of Posts and Telecommunications, Nan’an DistinctChongQing CityP.R. China
  3. 3.Department of Computer EducationSeowon UniversityChungbukKorea

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