Clustering spatial data with a hybrid em approach

Pattern Analysis and Applications

Volumn 8, Issue 1-2, 2005, Pages 139-148

  Hu, Tianming ^a   Sung, Sam Yuan ^b  

^a HANSHAN NORMAL UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Expectation maximization algorithm; Gaussian mixture; Penalized likelihood; Spatial autocorrelation; Spatial clustering; Spatial penalty term

Indexed keywords

EID: 27844452957     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-005-0251-8     Document Type: Article

References (27)

1. Ambroise C, Govaert G (1998) Convergence of an EM-type algorithm for spatial clustering. Pattern Recognit Lett 19(10):919-927
2. Basu S, Bilenko M, Mooney RJ (2004) A probabilistic framework for semi-supervised clustering. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 59-68
3. Celeux G, Govaert G (1992) A classification EM algorithm for clustering and two stochastic versions. Comput Stat Data Anal 14(3):315-332
4. Cressie NA (1993) Statistics for spatial data. Wiley, New York
5. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B(39):1-38
6. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on knowledge discovery and data mining, pp 226-231
7. Estivill-Castro V, Lee I (2001) Fast spatial clustering with different metrics and in the presence of obstacles. In: Proceedings of the 9th ACM international symposium on aAdvances in geographic information systems, pp 142-147
8. Friedman N (1998) The Bayesian structural EM algorithm. In: Proceedings of the 14th conference on uncertainty in artificial intelligence, pp 129-138
9. Geman S, Geman G (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Machine Intell 6:721-741
10. Gilley OW, Pace RK (1996) On the harrison and rubinfeld data. J Environ Econ Management 31:403-405
11. Guo D, Peuquet D, Gahegan M (2002) Opening the black box: Interactive hierarchical clustering for multivariate spatial patterns. In: Proceedings of the 10th ACM international symposium on advances in geographic information systems, pp 131-136
12. Hathaway RJ (1986) Another interpretation of the EM algorithm for mixture distributions. Stat Probabil Lett 4:53-56
13. Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation using Gabor filters. Pattern Recognit 24(12):1167-1186
14. Karypis G, Han EH, Kumar V (1999) CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. Computer 32(8):68-75
15. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York
16. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:79-86
17. Legendre P (1987) Constrained clustering. In: Legendre P, Legendre L (eds) Developments in numerical ecology, pp 289-307. NATO ASI Series G 14
18. LeSage JP (1999) MATLAB Toolbox for spatial econometrics. http://www.spatial-econometrics.com
19. Mceliece R (1977) Theory of information and coding. Addison-Wesley, Reading
20. Murphy PM, Aha DW (1994) UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University of California at Irvine, http://www.ics.uci.cdu/~mlearn/MLRepository.html
21. Neal R, Hinton G (1998) A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan M (ed) Learning in graphical models. Kluwer, Dordrecht, pp 355-368
22. Neukirchen C, Rottland J, Willett D, Rigoll G (2001) A continuous density interpretation of discrete HMM systems and MMI-neural networks. IEEE Trans Speech Audio Processing 9(4):367-377
23. Ng R, Han J (2002) CLARANS: A method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003-1016
24. Pena JM, Lozano JA, Larranaga P (2000) An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. Pattern Recognit Lett 21(8):779-786
25. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
26. Rasson JP, Granville V (1995) Multivariate discriminant analysis and maximum penalized likelihood density estimation. J R Stat Soc B(57):501-517
27. Tung A, Hou J, Han J (2001) Spatial clustering in the presence of obstacles. In: Proceedings of 17th international conference on data engineering, pp 359-367