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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
This updated version of mclust adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
Parsimonious Gaussian mixture models
A class of eight parsimonious Gaussian mixture models which are based on the mixtures of factor analyzers model are introduced and the maximum likelihood estimates for the parameters in these models are found using an AECM algorithm.
Mixtures of distance-based models for ranking data
Mixtures of distance-based models are used to analyze ranking data from heterogeneous populations, including the Irish electoral system and the Irish college admission system.
Improved Bayesian inference for the stochastic block model with application to large networks
An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. This is known as block-modeling or
Variational Bayesian inference for the Latent Position Cluster Model for network data
Analyzing Networks and Learning with Graphs Workshop at 23rd annual conference on Neural Information Processing Systems (NIPS 2009), Whister, December 11 2009
A mixture of experts model for rank data with applications in election studies
A voting bloc is defined to be a group of voters who have similar voting preferences. The cleavage of the Irish electorate into voting blocs is of interest. Irish elections employ a "single
Analysis of Irish third‐level college applications data
The Irish college admissions system involves prospective students listing up to ten courses in order of preference on their application. Places in third level educational institutions are
A Latent Space Model for Rank Data
A latent space model is proposed for rank (voting) data, where both voters and candidates are located in the same D dimensional latent space, and the estimated candidate positions suggest that the party politics play an important role in this election.
Using unlabelled data to update classification rules with applications in food authenticity studies
An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what
Model-based clustering of microarray expression data via latent Gaussian mixture models
This modelling approach builds on previous work by introducing a modified factor analysis covariance structure, leading to a family of 12 mixture models, including parsimonious models, which gives very good performance, relative to existing popular clustering techniques, when applied to real gene expression microarray data.