Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …

The important role of finite mixture models in the statistical analysis of data is underscored
by the ever-increasing rate at which articles on mixture applications appear in the statistical …

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 …

Interest in the skew-normal and related families of distributions has grown enormously over
recent years, as theory has advanced, challenges of data have grown, and computational …

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 …

Mixture distributions, in particular normal mixtures, are applied to data with two main
purposes in mind. One is to provide an appealing semiparametric framework in which to …

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian
distributions, we present a joint approach to estimate the number of mixture components and …

We introduce a mixture of generalized hyperbolic distributions as an alternative to the
ubiquitous mixture of Gaussian distributions as well as their near relatives within which the …

Finite mixtures of skew distributions have emerged as an effective tool in modelling
heterogeneous data with asymmetric features. With various proposals appearing rapidly in …

Data clustering has received a lot of attention and numerous methods, algorithms and
software packages are available. Among these techniques, parametric finite-mixture models …