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Found 25 papers, 4 papers with code
Clustering Higher Order Data: An Application to Pediatric Multi-variable Longitudinal Data
Physical activity levels are an important predictor of cardiovascular health and increasingly being measured by sensors, like accelerometers.
Finding Outliers in Gaussian Model-Based Clustering
Unsupervised classification, or clustering, is a problem often plagued by outliers, yet there is a paucity of work on handling outliers in unsupervised classification.
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed by including a penalty term in the likelihood constraining the parameters resulting in a flexible model for high dimensional data and a meaningful interpretation.
Clustering Discrete-Valued Time Series
The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete-valued time series data.
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes
Functional data analysis is a statistical framework where data are assumed to follow some functional form.
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering
An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering.
Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers
In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering.
Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data
Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data.
Methodology
A Latent Gaussian Mixture Model for Clustering Longitudinal Data
Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data.
Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models
A mixture of first-order continuous time Markov models is introduced for unsupervised and semi-supervised learning of clickstream data.
A Mixture of Matrix Variate Bilinear Factor Analyzers
This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification.
A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data
The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.
Methodology Quantitative Methods Computation
Multivariate response and parsimony for Gaussian cluster-weighted models
A family of parsimonious Gaussian cluster-weighted models is presented.
Model Based Clustering of High-Dimensional Binary Data
Recent work on clustering of binary data, based on a $d$-dimensional Gaussian latent variable, is extended by incorporating common factor analyzers.
Asymmetric Clusters and Outliers: Mixtures of Multivariate Contaminated Shifted Asymmetric Laplace Distributions
Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein).
Families of Parsimonious Finite Mixtures of Regression Models
Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies.
A Mixture of Generalized Hyperbolic Factor Analyzers
Model-based clustering imposes a finite mixture modelling structure on data for clustering.
Parsimonious Shifted Asymmetric Laplace Mixtures
A family of parsimonious shifted asymmetric Laplace mixture models is introduced.
Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations.
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures
This mixture model-based approach is based on fitting generalized hyperbolic mixtures on a reduced subspace within the paradigm of model-based clustering, classification, or discriminant analysis.
Standardizing Interestingness Measures for Association Rules
However, properties of individual association rules restrict the values an interestingness measure can achieve.
Mixtures of Common Skew-t Factor Analyzers
A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data.
Fractionally-Supervised Classification
When some observations are unlabelled, it can be very difficult to \textit{a~priori} choose the optimal level of supervision, and the consequences of a sub-optimal choice can be non-trivial.
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting
Within the family setting, model selection involves choosing the member of the family, i. e., the appropriate covariance structure, in addition to the number of mixture components.
Mixture Model Averaging for Clustering
In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one.
