Abstract
Spatial clustering is an important topic in knowledge discovery research. However, most clustering methods do not consider semantic information during the clustering process. In this paper, we present ONTO_CLUST, a framework for ontology-based spatial clustering. Using the framework, spatial clustering can be conducted with the support of a spatial clustering ontology. As an illustration, the framework is applied to the problem of clustering Canadian population data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD Record 27(2), 94–105 (1998)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. of 2nd KDD, Portland, pp. 226–231 (1996)
Estivill-Castro, V., Lee, I.J.: AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. In: Proc. of Intl. Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, Lyon, France, pp. 133–146 (2000)
Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5(2), 199–220 (1993)
Hwang, J.: Ontology-Based Spatial Clustering Method: Case Study of Traffic Accidents. Student Paper Sessions, UCGIS Summer Assembly (2003)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Chichester (1990)
Ng, R., Han, J.: Efficient and Effective Clustering Method for Spatial Data Mining. In: Proc. of Int’l Conf. on Very Large Data Bases, Santiago, Chile, pp. 144–155 (1994)
Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall, Englewood Cliffs (2003)
Sund, R.: Utilisation of administrative registers using scientific knowledge discovery. Intelligent Data Analysis 7(6), 501–519 (2003)
Tung, A.K.H., Han, J., Lakshmanan, L.V.S., Ng, R.T.: Constraint-Based Clustering in Large Databases. In: Proc. 2001 Intl. Conf. on Database Theory, London, U.K, pp. 405–419 (2001)
Tung, A.K.H., Hou, J., Han, J.: Spatial Clustering in the Presence of Obstacles. In: Proc. 2001 Intl. Conf. On Data Engineering, Heidelberg, Germany, pp. 359–367 (2001)
Wang, X., Hamilton, H.J.: Clustering Spatial Data in the Presence of Obstacles. In: Proc. 2004 FLAIRS, Miami Beach, Florida, pp. 312–317 (2004)
Wang, X., Hamilton, H.J.: DBRS: A Density-Based Spatial Clustering Method with Random Sampling. In: PAKDD 2003, pp. 563–575 (2003)
Wang, X., Rostoker, C., Hamilton, H.J.: Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 446–458. Springer, Heidelberg (2004)
Wang, W., Yang, J., Muntz, R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proc. of 23rd VLDB, Athens, Greece, pp. 186–195 (1997)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Zaïane, 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)
Zhang, T., Ramakrishna, R., Livny, M.: BIRCH: An Efficient Data Clustering Method For Very Large Databases. SIGMOD Record 25(2), 103–114 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Hamilton, H.J. (2005). Towards an Ontology-Based Spatial Clustering Framework. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_21
Download citation
DOI: https://doi.org/10.1007/11424918_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
eBook Packages: Computer ScienceComputer Science (R0)