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 …

This manuscript presents a comprehensive review of high‐resolution mass spectrometry in
the field of food analysis and metabolomics. We have followed the historical evolution of …

The literature on non-normal model-based clustering has continued to grow in recent years.
The non-normal models often take the form of a mixture of component densities that offer a …

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

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 …

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 …

This paper introduces a finite mixture of canonical fundamental skew tt (CFUST) distributions
for a model-based approach to clustering where the clusters are asymmetric and possibly …

Given sufficiently many components, it is often cited that finite mixture models can
approximate any other probability density function (pdf) to an arbitrary degree of accuracy …

The majority of model-based clustering techniques is based on multivariate normal models
and their variants. In this paper copulas are used for the construction of flexible families of …

An expanded family of mixtures of multivariate power exponential distributions is introduced.
While fitting heavy-tails and skewness have received much attention in the model-based …