An improvement of the NEC criterion for assessing the number of clusters in a mixture model

Pattern Recognition Letters

Volumn 20, Issue 3, 1999, Pages 267-272

  Biernacki, Christophe ^a   Celeux, Gilles ^a   Govaert, Gérard ^b  

^a INRIA RHÔNE ALPES   (France)

^b NONE

Author keywords

Cluster analysis; Mixture model; Normalized entropy criterion

Indexed keywords

IMAGE ANALYSIS; INFORMATION THEORY; MATHEMATICAL MODELS;

CLUSTER ANALYSIS; NORMALIZED ENTROPY CRITERION (NEC);

PATTERN RECOGNITION;

CLUSTER ANALYSIS; ENTROPY; MATHEMATICAL METHOD;

EID: 0033105385     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(98)00144-5     Document Type: Article

References (5)

1. Biernacki, C., 1997. Choix de modèles en classification. Ph.D. thesis, Compiègne University of Technology.
2. Biernacki C., Govaert G. Using the classification likelihood to choose the number of clusters. Computing Science and Statistics. 29(2):1997;451-457.
3. Celeux G., Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification. 13:1996;195-212.
4. Cutler, A., Windham, M.P., 1993. Information-based validity functionals for mixture analysis. In: Bozdogan, H. (Ed.), Proceedings of the first US-Japan Conference on the Frontiers of Statistical Modeling. Kluwer Academic Publishers, Amsterdam, pp. 149-170.
5. Goodman L.A. Exploratory latent structure models using both identifiable and unidentifiable models. Biometrika. 61:1974;215-231.