How many clusters? Which clustering method? Answers via model-based cluster analysis

How many clusters? Which clustering method? Answers via model-based cluster analysis (1998)

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by Chris Fraley , Adrian E. Raftery

Venue:	THE COMPUTER JOURNAL
Citations:	207 - 21 self

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@ARTICLE{Fraley98howmany,
    author = {Chris Fraley and Adrian E. Raftery},
    title = {How many clusters? Which clustering method? Answers via model-based cluster analysis},
    journal = {THE COMPUTER JOURNAL},
    year = {1998},
    volume = {41},
    number = {8},
    pages = {578--588}
}

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Citations

1581	Estimating the dimension of a model - SCHWARZ - 1978
1255	Some methods for classification and analysis of multivariate observations - MacQueen - 1967
1117	Clustering Algorithms - Hartigan - 1975
917	Pattern Recognition with Fuzzy Objective Function Algorithms - Bezdek - 1981
726	The EM algorithm and extensions - McLachlan, Krishnan - 1997
717	Bayes factors - Kass, Raftery - 1995
416	Bayesian classification (AutoClass): Theory and results - Cheeseman, Stutz - 1996
376	Mixture Models: Inference and Applications to Clustering - McLachlan, Basford - 1988
326	Hierarchical Grouping to optimize an objective function - Ward - 1963
253	Raftery A: Model-based Gaussian and non-Gaussian clustering. Biometrics - Banfield - 1993
247	Principal curves - Hastie, Stuetzle - 1989
245	On the convergence properties of the EM algorithm - Wu - 1983
155	Finding groups in data - Kaufman, Rousseeuw - 1990
155	Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables - Chickering, Heckerman - 1997
119	Neural networks and related methods for classification - Ripley - 1994
88	L: Practical bayesian density estimation using mixtures of Normals - Roeder, Wasserman - 1997
82	A classification EM algorithm for clustering and two stochastic versions - Celleux, Govaert - 1992
78	Gaussian parsimonious clustering models - Celeux, Govaert - 1995
75	Maximum likelihood incomplete data via the EM algorithm - Dempster, Laird, et al. - 1977
68	Detecting features in spatial point processes with clutter via model-based clustering - Dasgupta, Raftery - 1998
58	The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem - Celeux, G, et al. - 1985
52	On some invariant criteria for grouping data - Friedman, Rubin - 1967
51	Multidimensional clustering algorithms - Murtagh - 1985
51	Clustering methods based on likelihood ratio criteria - Scott, Symons - 1971
44	Classi®cation: Methods for the Exploratory Analysis of Multivariate Sata - Gordon - 1981
43	Algorithms for model-based Gaussian hierarchical clustering - Fraley - 1998
41	Nearest-neighbor clutter removal for estimating features in spatial point processes - Byers, Raftery - 1998
36	On the convergence of the EM algorithm - Boyles - 1983
36	ªIce Floe Identification in Satellite Images Using Mathematical Morphology and Clustering about Principal Curves,º - Banfield, Raftery - 1992
31	Clustering criteria and multivariate normal mixtures. Biometrics - Symons - 1981
30	dissection and analysis: theory, FORTRAN programs examples - Cluster - 1985
29	Consistent estimation of a mixing distribution - Leroux - 1992
29	Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition - Bensmail, Celeux - 1996
26	Linear flaw detection in woven textiles using model-based clustering - Campbell, Fraley, et al. - 1997
25	Nonparametric maximum likelihood estimation of features in spatial point processes using Voronoï tessellation - Allard, Fraley - 1997
24	Fitting straight lines to point patterns - Murtagh, Raftery - 1984
22	Hierarchical model-based clustering for large datasets - Posse - 1999
21	Inference in Model-Based Cluster Analysis - Bensmail, Celeux, et al. - 1997
20	Choosing the number of component clusters in the mixture model using a new informational complexity criterion of the inverse fisher information matrix - Bozdogan - 1993
18	Bayesian Cluster Analysis - Binder - 1978
18	Theory of Probability, 3rd edn - Jeffreys - 1961
17	Robust cluster analysis via mixtures of multivariate t-distributions - McLachlan, Peel
16	A.E.: Principal curve clustering with noise - Stanford, Raftery - 2000
15	Mathematical morphology: a modern approach in image processing based on algebra and geometry - Heijmans - 1995
12	An attempt to define the nature of chemical diabetes using a multidimensional analysis - Reaven, Miller - 1979
12	Percentage Points of a Test for Clusters - Engelman, Hartigan - 1969
11	AutoClass—a Bayesian approach to classification - Stutz, Cheeseman - 1995
8	Reconnaissance de mélanges de densités par un algorithme d’apprentissage probabiliste - Broniatowski, Celeux, et al. - 1984
8	The classification and mixture maximum likelihood approaches to cluster analysis - McLachlan - 1982
6	Nonparametric Minefield Detection and Localization - Muise, Smith - 1991