DGCL: An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database
Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set makes the clustering process extremely costly. In this paper, we propose DGCL, an enhanced Density-Grid based Clustering algorithm for Large spatial database. The characteristics of dense area can be enhanced by considering the affection of the surrounding area. Dense areas are analytically identified as clusters by removing sparse area or outliers with the help of a density threshold. Synthetic datasets are used for testing and the result shows the superiority of our approach.
KeywordsCluster Algorithm Spatial Data Spatial Database Base Cluster Algorithm Spatial Data Mining
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- 1.Han, J., Kamber, M.: < <Data Mining: Concepts and Techniques> >. Academic Press, London (2001)Google Scholar
- 2.El-Sonbaty, Y., Ismail, M.A., Farouk, M.: An Efficient Density Based Clustering Algorithm for Large Databases. In: ICTAI (2004)Google Scholar
- 4.Pilevar, A.H., Sukumar, M.: GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases. Elsevier B.V, Amsterdam (2004)Google Scholar
- 5.Zhao, Y., Song, J.: GDILC: A Grid-based Density-Isoline Clustering algorithm. IEEE, Los Alamitos (2001)Google Scholar
- 6.Xu, X., Ester, M., Kriegel, H.-p., Sander, J.: Clustering and Knowledge Discovery in Spatial Databases (1997)Google Scholar
- 7.Zhao, Y., Zhang, C., Shen, Y.-D.: Clustering High-Dimensional Data with Low-Order Neighbors. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004 (2004)Google Scholar
- 8.Qian, Y., Zhang, K.: GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering. In: SAC 2004, Nicosia, Cyprus, March 14-17 (2004)Google Scholar
- 9.Qian, Y., Zhang, G., Zhang, K.: FACADE: A Fast and Effective Approach to the Discovery of Dense Clusters in Noise Spatial Data. In: SIGMOD 2004 (2004)Google Scholar