[图书][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 …
Practical and theoretical aspects of mixture‐of‐experts modeling: An overview
HD Nguyen, F Chamroukhi - Wiley Interdisciplinary Reviews …, 2018 - Wiley Online Library
Mixture‐of‐experts (MoE) models are a powerful paradigm for modeling data arising from
complex data generating processes (DGPs). In this article, we demonstrate how different …
complex data generating processes (DGPs). In this article, we demonstrate how different …
Model-based clustering via linear cluster-weighted models
A novel family of twelve mixture models with random covariates, nested in the linear t cluster-
weighted model (CWM), is introduced for model-based clustering. The linear t CWM was …
weighted model (CWM), is introduced for model-based clustering. The linear t CWM was …
High-dimensional regression with gaussian mixtures and partially-latent response variables
The problem of approximating high-dimensional data with a low-dimensional representation
is addressed. The article makes the following contributions. An inverse regression …
is addressed. The article makes the following contributions. An inverse regression …
Erratum to: The generalized linear mixed cluster-weighted model
Cluster-weighted models (CWMs) are a flexible family of mixture models for fitting the joint
distribution of a random vector composed of a response variable and a set of covariates …
distribution of a random vector composed of a response variable and a set of covariates …
Matrix normal cluster-weighted models
Finite mixtures of regressions with fixed covariates are a commonly used model-based
clustering methodology to deal with regression data. However, they assume assignment …
clustering methodology to deal with regression data. However, they assume assignment …
Gaussian parsimonious clustering models with covariates and a noise component
We consider model-based clustering methods for continuous, correlated data that account
for external information available in the presence of mixed-type fixed covariates by …
for external information available in the presence of mixed-type fixed covariates by …
Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
A Punzo, PD McNicholas - Journal of Classification, 2017 - Springer
The Gaussian cluster-weighted model (CWM) is a mixture of regression models with random
covariates that allows for flexible clustering of a random vector composed of response …
covariates that allows for flexible clustering of a random vector composed of response …
Clustering and classification via cluster-weighted factor analyzers
In model-based clustering and classification, the cluster-weighted model is a convenient
approach when the random vector of interest is constituted by a response variable Y and by …
approach when the random vector of interest is constituted by a response variable Y and by …