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A New and Efficient K-Medoid Algorithm for Spatial Clustering

  • Qiaoping Zhang
  • Isabelle Couloigner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3482)

Abstract

A new k-medoids algorithm is presented for spatial clustering in large applications. The new algorithm utilizes the TIN of medoids to facilitate local computation when searching for the optimal medoids. It is more efficient than most existing k-medoids methods while retaining the exact the same clustering quality of the basic k-medoids algorithm. The application of the new algorithm to road network extraction from classified imagery is also discussed and the preliminary results are encouraging.

Keywords

Spatial Cluster Cluster Quality Multispectral Imagery Road Extraction Road Centerline 
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 2005

Authors and Affiliations

  • Qiaoping Zhang
    • 1
  • Isabelle Couloigner
    • 1
  1. 1.Department of Geomatics EngineeringUniversity of CalgaryCalgary, AlbertaCanada

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