메뉴 건너뛰기




Volumn 52, Issue 1, 2007, Pages 520-536

Model-based methods to identify multiple cluster structures in a data set

Author keywords

Cluster analysis; Cluster structure; Clustering variables; Mixture model; Model selection

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; MATHEMATICAL MODELS; MATRIX ALGEBRA; MONTE CARLO METHODS;

EID: 34548214332     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2007.02.019     Document Type: Article
Times cited : (32)

References (29)
  • 1
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • Petrov B.N., and Csaki B.F. (Eds), Academiai Kiado, Budapest
    • Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov B.N., and Csaki B.F. (Eds). Second International Symposium on Information Theory (1973), Academiai Kiado, Budapest 267-281
    • (1973) Second International Symposium on Information Theory , pp. 267-281
    • Akaike, H.1
  • 2
    • 34548225185 scopus 로고    scopus 로고
    • Combinatorial search in multivariate statistics
    • Banks D., and Olszewski R.T. Combinatorial search in multivariate statistics. Statist. Trans. 5 5 (2002) 803-821
    • (2002) Statist. Trans. , vol.5 , Issue.5 , pp. 803-821
    • Banks, D.1    Olszewski, R.T.2
  • 3
    • 30344471882 scopus 로고    scopus 로고
    • A generalized clustering problem with application to DNA microarrays
    • Article 2
    • Belitskaya-Levy I. A generalized clustering problem with application to DNA microarrays. Statist. Appl. Genetics Molecular Biol. 5 1 (2006) Article 2
    • (2006) Statist. Appl. Genetics Molecular Biol. , vol.5 , Issue.1
    • Belitskaya-Levy, I.1
  • 4
    • 0034228914 scopus 로고    scopus 로고
    • Assessing a mixture model for clustering with the integrated classification likelihood
    • Biernacki C., Celeux G., and Govaert G. Assessing a mixture model for clustering with the integrated classification likelihood. IEEE Trans. Pattern Anal. Mach. Intelligence 22 7 (2000) 719-725
    • (2000) IEEE Trans. Pattern Anal. Mach. Intelligence , vol.22 , Issue.7 , pp. 719-725
    • Biernacki, C.1    Celeux, G.2    Govaert, G.3
  • 5
    • 34250108028 scopus 로고
    • Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions
    • Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52 3 (1987) 345-370
    • (1987) Psychometrika , vol.52 , Issue.3 , pp. 345-370
    • Bozdogan, H.1
  • 8
    • 0035998835 scopus 로고    scopus 로고
    • Model-based clustering, discriminant analysis and density estimation
    • Fraley C., and Raftery A.E. Model-based clustering, discriminant analysis and density estimation. J. Amer. Statist. Assoc. 97 (2002) 611-631
    • (2002) J. Amer. Statist. Assoc. , vol.97 , pp. 611-631
    • Fraley, C.1    Raftery, A.E.2
  • 9
    • 34548281843 scopus 로고    scopus 로고
    • Fraley, C., Raftery, A.E., 2002b. MCLUST: software for model-based clustering, density estimation and discriminant analysis. Technical Report No. 415, Department of Statistics, University of Washington.
  • 10
    • 0742306126 scopus 로고    scopus 로고
    • Enhanced software for model-based clustering
    • Fraley C., and Raftery A.E. Enhanced software for model-based clustering. J. Classification 20 (2003) 263-286
    • (2003) J. Classification , vol.20 , pp. 263-286
    • Fraley, C.1    Raftery, A.E.2
  • 11
    • 8644255832 scopus 로고    scopus 로고
    • Clustering objects on subsets of attributes
    • Friedman J.H., and Meulman J.J. Clustering objects on subsets of attributes. J. Roy. Statist. Soc. B 66 (2004) 815-849
    • (2004) J. Roy. Statist. Soc. B , vol.66 , pp. 815-849
    • Friedman, J.H.1    Meulman, J.J.2
  • 12
    • 0000930629 scopus 로고
    • Graph-theoretic measures of multivariate association and prediction
    • Friedman J.H., and Rafsky L. Graph-theoretic measures of multivariate association and prediction. Ann. Statist. 11 (1983) 377-391
    • (1983) Ann. Statist. , vol.11 , pp. 377-391
    • Friedman, J.H.1    Rafsky, L.2
  • 13
    • 34548231214 scopus 로고    scopus 로고
    • Identifying multiple cluster structures through latent class models
    • Spiliopoulou M., Kruse R., Borgelt C., Nürnberger A., and Gaul W. (Eds), Springer, Berlin, Heidelberg
    • Galimberti G., and Soffritti G. Identifying multiple cluster structures through latent class models. In: Spiliopoulou M., Kruse R., Borgelt C., Nürnberger A., and Gaul W. (Eds). From Data and Information Analysis to Knowledge Engineering (2006), Springer, Berlin, Heidelberg 174-181
    • (2006) From Data and Information Analysis to Knowledge Engineering , pp. 174-181
    • Galimberti, G.1    Soffritti, G.2
  • 15
    • 0034568109 scopus 로고    scopus 로고
    • Gene shaving as a method for identifying distinct sets of genes with similar expression patterns
    • Hastie T., Tibshirani R., Eisen M.B., Alizadeh A., et al. Gene shaving as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 1 (2000) 1-21
    • (2000) Genome Biol. , vol.1 , pp. 1-21
    • Hastie, T.1    Tibshirani, R.2    Eisen, M.B.3    Alizadeh, A.4
  • 17
    • 0000146283 scopus 로고
    • Discarding variables in a principal component analysis, I: artificial data
    • Jolliffe I.T. Discarding variables in a principal component analysis, I: artificial data. Appl. Statist. 21 (1972) 160-173
    • (1972) Appl. Statist. , vol.21 , pp. 160-173
    • Jolliffe, I.T.1
  • 19
    • 16544376973 scopus 로고    scopus 로고
    • Agglomerative hierarchical clustering of continuous variables based on mutual information
    • Kojadinovic I. Agglomerative hierarchical clustering of continuous variables based on mutual information. Comput. Statist. Data Anal. 46 (2004) 269-294
    • (2004) Comput. Statist. Data Anal. , vol.46 , pp. 269-294
    • Kojadinovic, I.1
  • 20
    • 0036012349 scopus 로고    scopus 로고
    • Plaid models for gene expression data
    • Lazzeroni L., and Owen A. Plaid models for gene expression data. Statist. Sinica 12 (2002) 61-86
    • (2002) Statist. Sinica , vol.12 , pp. 61-86
    • Lazzeroni, L.1    Owen, A.2
  • 21
    • 0035741575 scopus 로고    scopus 로고
    • Latent class factor and cluster models, biplots and related graphical displays
    • Magidson J., and Vermunt J.K. Latent class factor and cluster models, biplots and related graphical displays. Sociological Methodol. 31 (2001) 223-264
    • (2001) Sociological Methodol. , vol.31 , pp. 223-264
    • Magidson, J.1    Vermunt, J.K.2
  • 23
    • 16544367516 scopus 로고    scopus 로고
    • Some trends in the classification of variables
    • Hayashi C., Oshumi N., Yajima K., Tanaka Y., Bock H.-H., and Baba Y. (Eds), Springer, Berlin, Heidelberg
    • Nicolau F.C., and Bacelar-Nicolau H. Some trends in the classification of variables. In: Hayashi C., Oshumi N., Yajima K., Tanaka Y., Bock H.-H., and Baba Y. (Eds). Data Science, Classification, and Related Methods (1998), Springer, Berlin, Heidelberg 89-98
    • (1998) Data Science, Classification, and Related Methods , pp. 89-98
    • Nicolau, F.C.1    Bacelar-Nicolau, H.2
  • 24
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • Rand W.M. Objective criteria for the evaluation of clustering methods. J. Amer. Statist. Assoc. 66 (1971) 846-850
    • (1971) J. Amer. Statist. Assoc. , vol.66 , pp. 846-850
    • Rand, W.M.1
  • 25
    • 34548224414 scopus 로고    scopus 로고
    • SAS Institute Inc. 1999. SAS/STAT User's Guide. Cary, NC: SAS Institute Inc.
  • 26
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwartz G. Estimating the dimension of a model. Ann. Statist. 6 2 (1978) 461-464
    • (1978) Ann. Statist. , vol.6 , Issue.2 , pp. 461-464
    • Schwartz, G.1
  • 27
    • 28244449047 scopus 로고    scopus 로고
    • Hierarchical clustering of variables: a comparison among strategies of analysis
    • Soffritti G. Hierarchical clustering of variables: a comparison among strategies of analysis. Commun. Statist.: Simulation Comput. 28 4 (1999) 977-999
    • (1999) Commun. Statist.: Simulation Comput. , vol.28 , Issue.4 , pp. 977-999
    • Soffritti, G.1
  • 28
    • 0242287940 scopus 로고    scopus 로고
    • Identifying multiple cluster structures in a data matrix
    • Soffritti G. Identifying multiple cluster structures in a data matrix. Commun. Statist.: Simulation Comput. 32 4 (2003) 1151-1177
    • (2003) Commun. Statist.: Simulation Comput. , vol.32 , Issue.4 , pp. 1151-1177
    • Soffritti, G.1
  • 29


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.