Abstract
Spatial Data Mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This chapter provides an overview on the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research.
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References
Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proc. of Very Large Databases.
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer, Dordrecht, Netherlands.
Anselin, L. (1994). Exploratory Spatial Data Analysis and Geographic Information Systems. In Painho, M., editor, New Tools for Spatial Analysis, pages 45–54.
Anselin, L. (1995). Local Indicators of Spatial Association: LISA. Geographical Analysis, 27(2):93–115.
Barnett, V. and Lewis, T. (1994). Outliers in Statistical Data. John Wiley, 3rd edition edition.
Besag, J. (1974). Spatial Interaction and Statistical Analysis of Lattice Systems. Journal of Royal Statistical Society: Series B, 36:192–236.
Bolstad, P. (2002). GIS Foundamentals: A Fisrt Text on GIS. Eider Press.
Cressie, N. (1993). Statistics for Spatial Data (Revised Edition). Wiley, New York.
Estivill-Castro, V. and Lee, I. (2001). Data Mining Techniques for Autonomous Exploration of Large Volumes of Geo-referenced Crime Data. In Proc. of the 6th International Conference on Geocomputation.
Estivill-Castro, V. and Murray, A. (1998). Discovering Associations in Spatial Data-An Efficient Medoid Based Approach. In Proc. of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Han, J., Kamber, M., and Tung, A. (2001). Spatial Clustering Methods in Data Mining: A Survey. In Miller, H. and Han, J., editors, Geographic Data Mining and Knowledge Discovery. Taylor and Francis.
Hawkins, D. (1980). Identification of Outliers. Chapman and Hall.
Huang, Y., Shekhar, S., and Xiong, H. (2004). Discovering Co-location Patterns from Spatial Datasets:A General Approach. IEEE Transactions on Knowledge and Data Engineering, 16(12).
Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data. Prentice Hall.
Jhung, Y. and Swain, P. H. (1996). Bayesian Contextual Classification Based on Modified M-Estimates and Markov Random Fields. IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(l):67–75.
Koperski, K. and Han, J. (1995). Discovery of Spatial Association Rules in Geographic Information Databases. In Proc. Fourth International Symposium on Large Spatial Databases, Maine. 47–66.
Li, S. (1995). A Markov Random Field Modeling. Computer Vision.
Morimoto, Y. (2001). Mining Frequent Neighboring Class Sets in Spatial Databases. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
Ripley, B. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society, Series B 39:172–192.
Roddick, J.-F. and Spiliopoulou, M. (1999). A Bibliography of Temporal, Spatial and Spatio-Temporal Data Mining Research. SIGKDD Explorations 1(1): 34–38 (1999).
Shekhar, S. and Chawla, S. (2003). Spatial Databases: A Tour. Prentice Hall (ISBN 0-7484-0064-6).
Shekhar, S. and Huang, Y. (2001). Co-location Rules Mining: A Summary of Results. In Proc. of the 7th Int’l Symp. on Spatial and Temporal Databases.
Shekhar, S., Lu, C, and Zhang, P. (2003). A Unified Approach to Detecting Spatial Outliers. Geolnformatica, 7(2).
Shekhar, S., Schrater, P. R., Vatsavai, R. R., Wu, W., and Chawla, S. (2002). Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transaction on Multimedia, 4(2).
Solberg, A. H., Taxt, T., and Jain, A. K. (1996). A Markov Random Field Model for Classification of Multisource Satellite Imagery. IEEE Transaction on Geoscience and Remote Sensing, 34(1): 100–113.
Tobler, W. (1979). Cellular Geography, Philosophy in Geography. Gale and Olsson, Eds., Dordrecht, Reidel.
Warrender, C. E. and Augusteijn, M. F. (1999). Fusion of image classifications using Bayesian techniques with Markov rand fields. International Journal of Remote Sensing, 20(10): 1987–2002.
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Shekhar, S., Zhang, P., Huang, Y. (2005). Spatial Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_39
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DOI: https://doi.org/10.1007/0-387-25465-X_39
Publisher Name: Springer, Boston, MA
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