Variable selection methods for model-based clustering
Abstract Model-based clustering is a popular approach for clustering multivariate data which
has seen applications in numerous fields. Nowadays, high-dimensional data are more and …
has seen applications in numerous fields. Nowadays, high-dimensional data are more and …
[LIBRO][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
Model-based clustering
PD McNicholas - Journal of Classification, 2016 - Springer
The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
[LIBRO][B] Introduction to clustering
In this chapter, the basic concepts of clustering are introduced. Moreover, the most relevant
decisions to be made for the practical application of clustering methods are listed and briefly …
decisions to be made for the practical application of clustering methods are listed and briefly …
Mixtures of shifted asymmetriclaplace distributions
A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering
and classification. A variant of the EM algorithm is developed for parameter estimation by …
and classification. A variant of the EM algorithm is developed for parameter estimation by …
An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model
Abstract Machine learning (ML) models have become a key component in modern world
services. In decision-making domains where human expertise is crucial, for example, for …
services. In decision-making domains where human expertise is crucial, for example, for …
A survey of feature selection methods for Gaussian mixture models and hidden Markov models
Feature selection is the process of reducing the number of collected features to a relevant
subset of features and is often used to combat the curse of dimensionality. This paper …
subset of features and is often used to combat the curse of dimensionality. This paper …
A mixture of generalized hyperbolic factor analyzers
The mixture of factor analyzers model, which has been used successfully for the model-
based clustering of high-dimensional data, is extended to generalized hyperbolic mixtures …
based clustering of high-dimensional data, is extended to generalized hyperbolic mixtures …
Gaussian mixture copulas for high-dimensional clustering and dependency-based subty**
Motivation The identification of sub-populations of patients with similar characteristics, called
patient subty**, is important for realizing the goals of precision medicine. Accurate …
patient subty**, is important for realizing the goals of precision medicine. Accurate …
A mixture of variance-gamma factor analyzers
The mixture of factor analyzers model is extended to variance-gamma mixtures to facilitate
flexible clustering of high-dimensional data. The formation of the variance-gamma …
flexible clustering of high-dimensional data. The formation of the variance-gamma …