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A New and Efficient K-Medoid Algorithm for Spatial Clustering

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Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3482))

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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.

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References

  1. Bourke, P.: Efficient Triangulation Algorithm Suitable for Terrain Modeling (1989), http://astronomy.swin.edu.au/~pbourke/terrain/triangulate/ (accessed on December 10, 2004)

  2. Doucette, P., Agouris, P., Musavi, M., Stefanidis, A.: Automated Extraction of Linear Features from Aerial Imagery Using Kohonen Learning and GIS Data. In: Agouris, P., Stefanidis, A. (eds.) ISD 1999. LNCS, vol. 1737, pp. 20–33. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Doucette, P., Agouris, P., Stefanidis, A., Musavi, M.: Self-Organised Clustering for Road Extraction in Classified Imagery. ISPRS Journal of Photogrammetry & Remote Sensing 55, 347–358 (2001)

    Article  Google Scholar 

  4. Fortune, S.: A Sweepline Algorithm for Voronoï Diagrams. Algorithmica 2(2), 153–174 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  5. Guo, D., Peuquet, D., Gahegan, M.: ICEAGE: Interactive Clustering and Exploration of Large and High-dimensional Geodata. GeoInformatica 7(3), 229–253 (2003)

    Article  MATH  Google Scholar 

  6. Han, J., Kamber, M., Tung, A.: Spatial clustering methods in data mining: A survey. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery. Taylor & Francis Inc., London (2001)

    Google Scholar 

  7. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wilsy & Sons (1990)

    Google Scholar 

  8. Lawson, C.L.: Software for C1 Surface Interpolation. In: Rice, J.R. (ed.) Mathematical Software III, pp. 161–194. Academic Press, New York (1977)

    Google Scholar 

  9. Lee, D.T., Schachter, B.J.: Two Algorithms for Constructing a Delaunay Triangulation. International Journal of Computer and Information Sciences 9(3), 219–242 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  10. Ng, R., Han, J.: Efficient and Effective Clustering Methods for Spatial Data Mining. In: Proc. 20th International Conference on Very Large Databases, Santiago, Chile (1994)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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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

  • Online ISBN: 978-3-540-32045-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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