A mixture model for the classification of three-way proximity data

Computational Statistics and Data Analysis

Volumn 50, Issue 7, 2006, Pages 1625-1654

  Bocci, Laura ^a   Vicari, Donatella ^a   Vichi, Maurizio ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Consensus partition; Finite mixture of Gaussian densities

Indexed keywords

DATABASE SYSTEMS; MATHEMATICAL MODELS; NORMAL DISTRIBUTION; PROBLEM SOLVING; STATISTICAL METHODS; THEOREM PROVING;

CONSEUS PARTITION; DATA SETS; FINITE MIXTURE OF GAUSSIAN DENSITIES; MIXTURE MODELS;

COMPUTATIONAL METHODS;

EID: 30144431978     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2005.02.007     Document Type: Article

References (22)

1. P. Arabie, J.D. Carroll, and W.S. DeSarbo Three-Way Scaling and Clustering 1987 Sage Newbury Park, CA
2. J.D. Banfield, and A.E. Raftery Model based Gaussian and non-Gaussian clustering Biometrics 49 1993 803 821
3. K.E. Basford, and G.J. McLachlan The mixture model of clustering applied to three-way data J. Classification 2 1985 109 125
4. K.E. Basford, and G.J. McLachlan Mixture Models. Inference and Application to Clustering 1988 Marcel Dekker Inc. New York, Basel
5. H. Bozdogan Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions Psychometrika 52 1987 345 370
6. J.D. Carroll, and P. Arabie INDCLUS: an individual differences generalization of the ADCLUS model and the MAPCLUS algorithm Psychometrika 48 1983 157 169
7. J.D. Carroll, L.A. Clark, and W.S. DeSarbo The representation of three-way proximity data by single and multiple tree structure models J. Classification 1 1984 25 74
8. G. Celeux, and G. Govaert Gaussian parsimonious clustering models Pattern Recognition 28 5 1995 781 793
9. W. Gaul, and M. Schader Clusterwise aggregation of relations Appl. Stochastic Models Data Anal. 4 1988 273 282
10. A.D. Gordon, and M. Vichi Partitions of partitions J. Classification 15 1998 265 285
11. L. Hubert, and P. Arabie Comparing partitions J. Classification 2 1985 193 218
12. S.C. Johnson Hierarchical clustering schemes Psichometrika 32 1967 241 254
13. G. McLachlan, and D. Peel Finite Mixture Model 2000 Wiley New York
14. McQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Symposium on Mathematical Statistics and Probability, vol. I: Statistics, University of California Press, Berkeley, CA, pp. 281-297.
15. X.L. Meng, and D.B. Rubin Maximum likelihood estimation via the ECM algorithm: a general framework Biometrika 80 1993 267 278
16. J.O. Ramsay Maximum likelihood estimation in multidimensional scaling Psychometrika 42 1977 241 266
17. J.O. Ramsay Some statistical approaches to multidimensional scaling data J. Roy. Statist. Soc. A 145 3 1982 285 312
18. D. Vicari, and M. Vichi Non-hierarchical classification structures W. Gaul O. Opitz M. Schader Data Analysis. Series: Studies in Classification, Data Analysis and Knowledge Organization 2000 Springer Heidelberg 51 65
19. M. Vichi One mode classification of a three-way data matrix J. Classification 16 1999 27 44
20. M. Wedel, and W.S. DeSarbo Mixture of (constrained) ultrametric trees Psychometrika 63 4 1998 419 443
21. F.W. Young Scaling replicated conditional rank-order data D. Heise Sociological Methodology 1974 American Sociological Association
22. F.W. Young Multidimensional Scaling M.J. Norusis SPSS Professional Statistics 6.1 1994 SPSS Inc. Chicago, IL