Clustering using Monte Carlo Cross-Validation

Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Volumn , Issue , 1996, Pages 126-133

  Smyth, Padhraic ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; MONTE CARLO METHODS;

CLUSTER STRUCTURE; CLUSTERINGS; CROSS VALIDATION; DATA SET; ILL POSED PROBLEM; LIKELIHOOD RATIOS; NUMBER OF CLUSTERS; PENALIZED LIKELIHOOD; PENALIZED-LIKELIHOOD; PROBABILISTIC CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 0242581819     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Banfield, J. D., and Raftery, A. E. 1993. 'Model-based Gaussian and non-Gaussian clustering,' Biometrics, 49, 803-821.
2. Burman, P. 1989 'A comparative study of ordinary cross-validation, v-fold cross-validation, and the repeated learning-testing methods,' Biometrika, 76(3), 503-514.
3. Celeux, G., and Govaert, G. 1995. 'Gaussian parsimonious clustering models,' Pattern Recognition, 28(5), 781-793.
4. Cheeseman, P. and Stutz. J. 1996. 'Bayesian classification (AutoClass): theory and results,' in Advances in Knowledge Discovery and Data Mining, U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthu-rusamy (eds.), Cambridge, MA: AAAI/MIT Press, pp. 153-180.
5. Chickering, D. M., and Heckerman, D. 1996. 'Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network,' MSR-TR-96-08 Technical Report, Microsoft Research, Redmond, WA.
6. Chow, Y. S., Geman, S. and Wu, L. D. 1983. 'Consistent cross-validated density estimation,' The Annals of Statistics, 11(1), 25-38.
7. Diebolt, J. and Robert, C. P. 1994. 'Bayesian estimation of finite mixture distributions,' J. R. Stat. Soc. B, 56, 363-375.
8. Gelman, A., J. B. Carlin, H. S. Stern, D. B. Rubin. 1995. Bayesian Data Analysis, London, UK: Chapman and Hall.
9. Jain, A. K., and R. C. Dubes. 1988. Algorithms for Clustering Data, Englewood Cliffs, NJ: Prentice Hall.
10. Kohavi, R. 1995. 'A study of cross-validation and bootstrap for accuracy estimation and model selection,' Proc. Int. Joint. Conf. AI, Montreal.
11. McLachlan, G. J. and K. E. Basford. 1988. Mixture Models: Inference and Applications to Clustering, New York: Marcel Dekker.
12. Peterson, G. and Barney, H. 1952. 'Control methods used in the study of vowels,' . J. Acoust. Soc. Am., 24, 175-184.
13. Reaven, G. M., and Miller, R. G. 1979. 'An attempt to define the nature of chemical diabetes using a multi-dimensional analysis,' Diabetologia, 16, 17-24.
14. Ripley, B. D. 1994. 'Neural networks and related methods for classification (with discussion),' J. Roy. Stat. Soc. B, 56, 409-456.
15. Sclove, S. C. 1983. 'Application of the conditional population mixture model to image segmentation,' IEEE Trans. Patt. Anal. Mach. Intell., PAMI-5, 428-433.
16. Shao, J. 1993. 'Linear model selection by cross-validation,' J. Am. Stat. Assoc., 88(422), 486-494.
17. Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis, Chapman and Hall.
18. Symons, M. 1981. 'Clustering criteria and multivariate normal mixtures,' Biometrics, 37, 35-43.
19. Titterington, D. M., A. F. M. Smith, U. E. Makov. 1985. Statistical Analysis of Finite Mixture Distributions, Chichester, UK: John Wiley and Sons.