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Publication Detail
A local distribution based spatial clustering algorithm
  • Publication Type:
    Conference
  • Authors:
    Deng M, Liu Q, Li G, Cheng T
  • Publication date:
    14/12/2009
  • Published proceedings:
    Proceedings of SPIE - The International Society for Optical Engineering
  • Volume:
    7495
  • ISBN-13:
    9780819478061
  • Status:
    Published
  • Print ISSN:
    0277-786X
Abstract
Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential spatial association rules and outliers among the spatial data. Most existing spatial clustering algorithms only utilize the spatial distance or local density to find the spatial clusters in a spatial database, without taking the spatial local distribution characters into account, so that the clustered results are unreasonable in many cases. To overcome such limitations, this paper develops a new indicator (i.e. local median angle) to measure the local distribution at first, and further proposes a new algorithm, called local distribution based spatial clustering algorithm (LDBSC in abbreviation). In the process of spatial clustering, a series of recursive search are implemented for all the entities so that those entities with its local median angle being very close or equal are clustered. In this way, all the spatial entities in the spatial database can be automatically divided into some clusters. Finally, two tests are implemented to demonstrate that the method proposed in this paper is more prominent than DBSCAN, as well as that it is very robust and feasible, and can be used to find the clusters with different shapes. © 2009 Copyright SPIE - The International Society for Optical Engineering.
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