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Simple Spatial Clustering Algorithm Based on R-tree

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7694))

Abstract

In this article, we present an algorithm based on R-tree structure to solve a clustering task in spatial data mining. The algorithm can apply to cluster not only point objects but also extended spatial objects such as lines and polygons. The experimental results show that our algorithm yields the same result as any other algorithm and accommodates to clustering task in spatial database.

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References

  1. Data Mining – Know It All. Morgan Kaufmann Publishers (2009)

    Google Scholar 

  2. Geographic Data Mining and Knowledge Discovery, 2nd edn. CRC Press (2009)

    Google Scholar 

  3. Pham, D.T., Afify, A.A.: Clustering techniques and their applications in engineering. Submitted to Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (2006) (submitted)

    Google Scholar 

  4. Guttman, A.: R-tree: A dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, vol. 14(2) (June 1984)

    Google Scholar 

  5. Zălik, K.R., Zălik, B.: A sweep-line algorithm for spatial clustering. Journal of Advances in Engineering Software 40(6) (2009)

    Google Scholar 

  6. http://cs.joensuu.fi/sipu/datasets/

  7. Kaur, H., Chauhan, R., Afshar Alam, M.: Spatial Clustering Algorithm using R-tree. Jounal of Computing 3(2) (2011)

    Google Scholar 

  8. Bogorny, V., Palma, A.T., Engel, P.M., Alvares, L.O.: Weka-GDPM – Integrating Classical Data Mining Toolkit to Geographic Information Systems. In: SBBD Workshop on Data Mining Algorithms and Aplications (WAAMD 2006), Florianopolis, Brazil, October 16-20, pp. 9–16 (2006)

    Google Scholar 

  9. Xion, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A Framework for Discovering Co-location Patterns in Data sets with Extended Spatial Objects (2004)

    Google Scholar 

  10. May, M., Savinov, A.: An architecture for the SPIN! Spatial Data Mining Platform

    Google Scholar 

  11. Sardadi, M.M., Rahim, M.S.M., Jupri, Z., Daman, D.B.: Choosing R-tree or Quadtree Spatial Data Indexing in One Oracle Spatial Database System to Make Faster Showing Geographical Map in Mobile Geographical Information System Technology. International Journal of Human and Social Sciences (2009)

    Google Scholar 

  12. Moreira, A., Santos, M.Y., Carneiro, S.: Density-based clustering algorithms – DBSCAN and SNN (2005)

    Google Scholar 

  13. Joshi, D., Samal, A.K., Soh, L.: Density-Based Clustering of Polygons. In: IEEE Symposium Series on Computational Intelligence and Data Mining, pp. 171–178 (2009)

    Google Scholar 

  14. Jiao, L., Liu, Y.: Knowledge Discovery by Spatial Clustering based on Self-Organizing Feature Map and a Composite Distance Measure. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII(Part B2) (2008)

    Google Scholar 

  15. Ng, R.T., Han, J.: CLARANS: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering (2002)

    Google Scholar 

  16. Koperski, K., Adhikary, J., Han, J.: Knowledge discovery in spatial databases: Progress and Challenges. In: Proceedings of the SIGMID Workshop on Research Issue in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver, Canada (1996)

    Google Scholar 

  17. Koperski, K., Han, J.: Discovery of SpatialAssociation Rules in Geographic Information Databases. In: Proc. 4th Int. Symp.on Large Spatial Databases, pp. 47–66. Springer, Berlin (1995)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Nguyen Vinh, N., Le, B. (2012). Simple Spatial Clustering Algorithm Based on R-tree. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_22

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  • DOI: https://doi.org/10.1007/978-3-642-35455-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35454-0

  • Online ISBN: 978-3-642-35455-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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