Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the number of components in a finite mixture model: the special case of homogeneity
Computational Statistics & Data Analysis
Asymptotic theory for maximum likelihood in nonparametric mixture models
Computational Statistics & Data Analysis
An EM Algorithm for the Block Mixture Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Top 10 algorithms in data mining
Knowledge and Information Systems
Block clustering of contingency table and mixture model
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Computational aspects of fitting mixture models via the expectation-maximization algorithm
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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Matrices of binary or count data are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. These likelihood-based one-mode and two-mode fuzzy clusterings provide maximum likelihood estimation of parameters and the options of using likelihood ratio tests or information criteria for model comparison. Geometric developments focused on pattern detection give likelihood-based analogues of various techniques in multivariate analysis, including multidimensional scaling, association analysis, ordination, correspondence analysis, and the construction of biplots. Illustrative examples demonstrate the effectiveness of these visualisations for identifying patterns of ecological significance (e.g. abrupt versus slow species turnover).