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.
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Zhang, Q., Couloigner, I. (2005). A New and Efficient K-Medoid Algorithm for Spatial Clustering. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424857_20
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DOI: https://doi.org/10.1007/11424857_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25862-9
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