Abstract
On the basis of analyzing the deficiencies of traditional spatial data mining, a framework for spatial data mining with uncertainty has been founded. Four key problems have been analyzed, including uncertainty simulation of spatial data with Monte Carlo method, spatial autocorrelation measurement, discretization of continuous data based on neighbourhood EM algorithm and uncertainty assessment of association rules. Meanwhile, the experiments concerned have been performed using the environmental geochemistry data gotten from Dexing, Jiangxi province in China.
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
Di, K.C.: Spatial data mining and knowledge discovery. Wuhan University Press, Wuhan (2000)
Li, D.R., Wang, S.L., Li, D.Y.: Theories and technologies of spatial data knowledge discovery. In: Geomatics and Information Science of Wuhan University, vol. 3, pp. 221–233 (2002)
Koperski, K.: A progressive refinement approach to spatial data mining. Simon Fraser University, Canada (1999)
Miller, H.J., Han, J.W.: Geographic data mining and knowledge discovery. Taylor & Francis, London (2001)
Clementini, E., Felice, P.D., Koperski, K.: Mining multiple-level spatial association rules for objects with a broad boundary. Data & Knowledge Engineering 3, 251–270 (2000)
Wang, S.L., Shi, W.Z., Li, D.R.: A method of spatial data mining dealing with randomness and fuzziness. In: Proceedings of the 2nd International Symposium on Spatial Data Quality, pp. 370–383 (2003)
Beaubouef, T., Ladner, R., Petry, F.: Rough set spatial data modeling for data mining. International Journal of Intelligent Systems 7, 567–584 (2004)
He, B.B., Fang, T., Guo, D.Z.: Uncertainty and its propagation in spatial data mining. Journal of Data Acquisition and Processing 4, 475–480 (2004)
Press, W.H.: Numerical recipes: The art of scientific computing, 2nd edn. Cambridge University Press, London (1996)
Box, G.E.P., Muller, M.E.: A Note on the Generation of Random Normal Deviates. The Annals of Mathematical Statistics 29, 610–611 (1958)
Goodchild, M.F.: Issuees of quality and uncertainty. In: Muller, J.C. (ed.) Advances In Cartography, pp. 113–139. Elsevier, London (1991)
CCSM(Canadian Council on Surveying and Mapping): National standards for the exchange of digital topographic data, II-standards for the quality evaluation of digital topographic data, Canada (1984)
Sadahiro, Y.: Cluster detection in uncertain point distributions: a comparison of four methods. Computers, Environment and Urban Systems 27, 33–52 (2003)
Ambroise, C., Dang, V., Govaert, G.: Clustering of spatial data by the EM algorithm. Quantitative Geology and Geostatistics 9, 493–504 (1997)
Hathaway, R.J.: Another interpretation of the EM algorithm for mixture distributions. Journal of Statistics & Probability Letters 4, 53–56 (1986)
Vazirgiannis, M., Halkidi, M., Gunopulos, D.: Uncertainty handling and quality assessment in data mining. Springer, London (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, B., Chen, C. (2007). Spatial Data Mining with Uncertainty. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-74377-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74376-7
Online ISBN: 978-3-540-74377-4
eBook Packages: Computer ScienceComputer Science (R0)