Abstract
In this paper, we combine sampling technique with DBSCAN algorithm to cluster large spatial databases, two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN; and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large-scale spatial databases.
This work was supported by the NSF of China (Grant no. 69743001.
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References
M. Ester, H.P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd International Conference on Knowledge Discovering in Databases and Data Mining (KDD-96), Portland, Oregon, August 1996.
S. Zhou, et al. Combining sampling technique with DBSCAN algorithm for clustering large spatial databases. Technical Report of Computer Science Department, Fudan University, 1999.
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Zhou, S., Zhou, A., Cao, J., Wen, J., Fan, Y., Hu, Y. (2000). Combining Sampling Technique with DBSCAN Algorithm for Clustering Large Spatial Databases. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_20
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DOI: https://doi.org/10.1007/3-540-45571-X_20
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