Abstract
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by spatial association rules discovered at multiple granularity levels. Classification is probabilistic and is based on an extension of naïve Bayes classifiers to multi- relational data. The method is implemented in a Data Mining system tightly integrated with an object relational spatial database. It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of rules that support qualitative spatial reasoning. An application to real-world spatial data is reported. Results show that the use of different levels of granularity is beneficial.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Andrienko, G., Andrienko, N.: Exploration of heterogeneous spatial data using interactive geo-visualization tools: study of deprivation indices in North-West England. North-West England Report. IST European project SPIN!(Spatial Mining for Data of Public Interest)
Appice, A., Ceci, M., Lanza, A., Lisi, F.A., Malerba, D.: Discovery of Spatial Association Rules in Georeferenced Census Data: A Relational Mining Approach, Intelligent Data Analysis. Special issue of Mining Official Data 7(6) (2003)
Baralis, E., Garza, P.: Majority Classification by Means of Association Rules. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 35–46. Springer, Heidelberg (2003)
Clark, P., Matwin, S.: Using qualitative models to guide induction learning. In: Proceedings of International Conference of Machine Learning, pp. 49–56. Morgan Kaufmann, San Francisco (1993)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zeoones loss. Machine Learning 29(2-3), 103–130 (1997)
Dong, G., Zhang, X., Wong, L., Li, J.: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, p. 30. Springer, Heidelberg (1999)
Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer, Berlin (2001)
Egenhofer, M.J.: Reasoning about Binary Topological Relations. In: Proceedings of the Second Symposium on Large Spatial Databases, Zurich, Switzerland, pp. 143–160 (1991)
Egenhofer, M.J., Franzosa, R.: Point-Set Topological Spatial Relations. International Journal of Geographical Information Systems 5(2), 61–174 (1991)
Ester, M., Kriegel, H.P., Sander, J.: Spatial Data Mining: A Database Approach. In: Proceedings International Symposium on Large Databases, Berlin, pp. 47–66 (1997)
Fürnkranz, J., Flach, P.A.: An analysis of rule evaluation metrics. In: Proceedings of International Conference of Machine Learning, Morgan Kaufmann, San Francisco (2003)
Güting, R.H.: An introduction to spatial database systems. VLDB Journal 4(3) (1994)
Klösgen, W., May, M.: Spatial Subgroup Mining. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, p. 275. Springer, Heidelberg (2002)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Koperski, K.: Progressive Refinement Approach to Spatial Data Mining, Ph.D. thesis, Computing Science, Simon Fraser University (1999)
Lisi, F.A., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relations. Machine Learning (2004) (to appear)
Liu, B., Hsu, W., Ma, Y.: Integratine classification and association rule mining. In: Proceedings of Knowledge Discovery in Databases KDD 1998, New York (1998)
Ludl, M.C., Widmer, G.: Relative Unsupervised Discretization for Association Rule Mining. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 148–158. Springer, Heidelberg (2000)
Malerba, D., Appice, A., Vacca, N.: SDMOQL: An OQL-based Data Mining Query Language for Map Interpretation Tasks. In: Proc. of the Workshop on Database Technologies for Data Mining (DTDM 2002), Prague, Czech Republic, March 25-27 (2002)
Malerba, D., Esposito, F., Lanza, A., Lisi, F.A., Appice, A.: Empowering a GIS with Inductive Learning Capabilities: The Case of INGENS. Journal of Computers, Environment and Urban Systems 27, 265–281 (2003)
Malerba, D.: Learning Recursive Theories in the Normal ILP Setting. Fundamenta Informaticae 57(1), 39–77 (2003)
May, M.: Spatial Knowledge Discovery: The SPIN! System. In: Fullerton, K. (ed.) Proceedings of the 6th EC-GIS Workshop, Lyon, JRC, Ispra (2000)
Preparata, F., Shamos, M.: Computational Geometry: An Introduction. Springer, New York (1985)
Shekhar, S., Schrater, P.R., Vatsavai, R.R., Wu, W., Chawla, S.: Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transaction on Multimedia 4(2), 174–188 (2002)
Pazzani, M., Mani, S., Shankle, W.R.: Beyond concise and colorful: learning intelligible rules. In: Proceedings of Knowledge Discovery in Databases KDD 1997 (1997)
Wrobel, S.: Inductive logic programming for knowledge discovery in databases. In: Džeroski, S., Lavra, N. (eds.) Relational Data Mining, pp. 74–101. Springer, Berlin (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ceci, M., Appice, A., Malerba, D. (2004). Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Knowledge Discovery in Databases: PKDD 2004. PKDD 2004. Lecture Notes in Computer Science(), vol 3202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30116-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-30116-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23108-0
Online ISBN: 978-3-540-30116-5
eBook Packages: Springer Book Archive