Abstract
The previous spatial clustering methods calculate the distance value between two spatial objects using the Euclidean distance function, which cannot reflect the grid path, and their computational complexity is high in the presence of obstacles. Therefore, in this paper, we propose a novel spatial clustering algorithm called DBSCAN-MDO. It reflects the grid path in the real world using the Manhattan distance function and reduces the number of obstacles to be considered by grouping obstacles in accordance with MBR of each cluster and filtering obstacles that do not affect the similarity between spatial objects.
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Estivill-Castro, V., Lee, I.: AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. In: Roddick, J.F., Hornsby, K. (eds.) TSDM 2000. LNCS (LNAI), vol. 2007, pp. 133–146. Springer, Heidelberg (2001)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Zaiane, O.R., Lee, C.-H.: Clustering Spatial Data in the Presence of Obstacles: A Density-Based Approach. In: International Database Engineering and Applications Symposium (IDEAS’02), p. 214 (2002)
Tung, A.K.H., Hou, J., Han, J.: Spatial clustering in the presence of obstacles. In: Proc. 2001 Int. conf. On Data Engineering, ICDE’01 (2001)
Zaiane, O.R., Lee, C.H.: Clustering Spatial Data When Facing Physical Constraints. In: Proc. of the IEEE International Conf. on Data Mining, Maebashi City, Japan, pp. 737–740 (2002)
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Park, SH., Lee, JH., Kim, DH. (2007). Spatial Clustering Based on Moving Distance in the Presence of Obstacles. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_96
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DOI: https://doi.org/10.1007/978-3-540-71703-4_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
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