Abstract
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.
This research was supported by the MIC (Ministry of Information and Communication),Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: < <Data Mining: Concepts and Techniques> >. Academic Press, London (2001)
El-Sonbaty, Y., Ismail, M.A., Farouk, M.: An Efficient Density Based Clustering Algorithm for Large Databases. In: ICTAI (2004)
Ma, W.M., Eden, Chow, Tommy, W.S.: A new shifting grid clustering algorithm. Pattern Recognition 37(3), 503–514 (2004)
Pilevar, A.H., Sukumar, M.: GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases. Elsevier B.V, Amsterdam (2004)
Zhao, Y., Song, J.: GDILC: A Grid-based Density-Isoline Clustering algorithm. IEEE, Los Alamitos (2001)
Xu, X., Ester, M., Kriegel, H.-p., Sander, J.: Clustering and Knowledge Discovery in Spatial Databases (1997)
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)
Qian, Y., Zhang, K.: GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering. In: SAC 2004, Nicosia, Cyprus, March 14-17 (2004)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, H.S., Gao, S., Xia, Y., Kim, G.B., Bae, H.Y. (2006). DGCL: An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_31
Download citation
DOI: https://doi.org/10.1007/11775300_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35225-9
Online ISBN: 978-3-540-35226-6
eBook Packages: Computer ScienceComputer Science (R0)