Spatial data mining with uncertainty

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4456 LNAI, Issue , 2007, Pages 308-316

  He, Binbin ^a   Chen, Cuihua ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b CHENGDU UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES; AUTOCORRELATION; COMPUTER SIMULATION; MONTE CARLO METHODS; UNCERTAINTY ANALYSIS;

UNCERTAINTY ASSESSMENT; UNCERTAINTY SIMULATION;

DATA MINING;

EID: 38349063603     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74377-4_33     Document Type: Conference Paper

References (16)

1. Di, K.C.: Spatial data mining and knowledge discovery. Wuhan University Press, Wuhan (2000)
2. 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)
3. Koperski, K.: A progressive refinement approach to spatial data mining. Simon Fraser University, Canada (1999)
4. Miller, H.J., Han, J.W.: Geographic data mining and knowledge discovery. Taylor & Francis, London (2001)
5. 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)
6. 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)
7. Beaubouef, T., Ladner, R., Petry, F.: Rough set spatial data modeling for data mining. International Journal of Intelligent Systems 7, 567-584 (2004)
8. 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)
9. Press, W.H.: Numerical recipes: The art of scientific computing, 2nd edn. Cambridge University Press, London (1996)
10. 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)
11. Goodchild, M.F.: Issuees of quality and uncertainty. In: Muller, J.C. (ed.) Advances In Cartography, pp. 113-139. Elsevier, London (1991)
12. 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)
13. Sadahiro, Y.: Cluster detection in uncertain point distributions: a comparison of four methods. Computers, Environment and Urban Systems 27, 33-52 (2003)
14. Ambroise, C., Dang, V., Govaert, G.: Clustering of spatial data by the EM algorithm. Quantitative Geology and Geostatistics 9, 493-504 ( 1997)
15. Hathaway, R.J.: Another interpretation of the EM algorithm for mixture distributions. Journal of Statistics & Probability Letters 4, 53-56 (1986)
16. Vazirgiannis, M., Halkidi, M., Gunopulos, D.: Uncertainty handling and quality assessment in data mining. Springer-Verlag, London (2003)