Clustering large categorical data

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

Volumn 2336, Issue , 2002, Pages 257-263

  Jollois, François Xavier ^a   Nadif, Mohamed ^a  

^a UNIVERSITÉ DE LORRAINE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; PARAMETER ESTIMATION;

CATEGORICAL ATTRIBUTES; CLASSIFICATION EM; CLUSTERING METHODS; DISSIMILARITY MEASURES; ESTIMATED PARAMETER; LARGE CATEGORICAL DATUM; PROBABILISTIC APPROACHES; PROBABILISTIC MODELING;

K-MEANS CLUSTERING;

EID: 84945314569     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47887-6_25     Document Type: Conference Paper

References (13)

1. Celeux, G. and Govaert, G. (1992): A Classification EM Algorithm for Clustering and two Stochastic Versions. Computational Statistics & Data Analysis, 14, 315-332.
2. Dempster, A., Laird, N. and Rubin, D. (1977): Mixture Densities, Maximum Likelihood from incomplete data via the EM Algorithm. Journal of the Royal Statistical Society, 39, 1, 1-38.
3. Diday, E., Bochi, S., Brossier, G. and Celeux, G. (1980): Optimisation en Classification Automatique, Le Chesnay, INRIA.
4. Everitt, B. (1984): An introduction to Latent Variables Models, Chapman and Hall.
5. Forgy, E. W. (1965): Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification. Biometrics, 21, 3, 768.
6. Govaert, G. and Nadif, M. (1996): Comparison of the Mixture and the Classification Maximum Likelihood in Cluster Analysis with binary data. Comput. Statis. and Data Analysis, 23, 65-81.
7. Huang, Z. (1997): A Fast Clustering Algorithm to Cluster very large categorical data sets in Data Mining. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (SIGMOD-DMKD’97).
8. Huang, Z. (1998): Extensions to the k-means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 2, 283-304.
9. Mc Lachlan, G. J. and Basford, K. E. (1989): Mixture Models, Inference and Applications to Clustering, Marcel Dekker.
10. Mac Queen, J. B. (1967): Some Methods for Classification and Analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 281-297.
11. Nadif, M. and Marchetti, F. (1993): Classification de Données Qualitatives et Modèles. Revue de Statistique Appliquée, 41, 1, 55-69.
12. Ralambondrainy H. (1995): A Conceptual Version of the k-means Algorithm, Pattern Recognition Letters, 16, pp. 1147-1157.
13. Symons M. J. (1981): Clustering Criteria and Multivariate Normal Mixture, Biometrics, 27, pp 387-397