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
Data Mining – Know It All. Morgan Kaufmann Publishers (2009)
Geographic Data Mining and Knowledge Discovery, 2nd edn. CRC Press (2009)
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)
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)
Zălik, K.R., Zălik, B.: A sweep-line algorithm for spatial clustering. Journal of Advances in Engineering Software 40(6) (2009)
Kaur, H., Chauhan, R., Afshar Alam, M.: Spatial Clustering Algorithm using R-tree. Jounal of Computing 3(2) (2011)
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)
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)
May, M., Savinov, A.: An architecture for the SPIN! Spatial Data Mining Platform
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)
Moreira, A., Santos, M.Y., Carneiro, S.: Density-based clustering algorithms – DBSCAN and SNN (2005)
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)
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)
Ng, R.T., Han, J.: CLARANS: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering (2002)
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)
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)
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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
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