A new and efficient k-medoid algorithm for spatial clustering

Authors:
Qiaoping Zhang;Isabelle Couloigner
Affiliations:
Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada;Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Year:
2005

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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.