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 …

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 many financial applications, such as fraud detection, reject inference, and credit
evaluation, detecting clusters automatically is critical because it helps to understand the …

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 …

Understanding the diversity of cell types in the brain has been an enduring challenge and
requires detailed characterization of individual neurons in multiple dimensions. To …

June 2012 mclust is a contributed R package for model-based clustering, classification, and
density estimation based on finite normal mixture modeling. It provides functions for …

" This is a great overview of the field of model-based clustering and classification by one of
its leading developers. McNicholas provides a resource that I am certain will be used by …

A general framework for solving image inverse problems with piecewise linear estimations is
introduced in this paper. The approach is based on Gaussian mixture models, which are …

Data with mixed-type (metric–ordinal–nominal) variables are typical for social stratification,
ie partitioning a population into social classes. Approaches to cluster such data are …

Finite mixture models have a long history in statistics, having been used to model population
heterogeneity, generalize distributional assumptions, and lately, for providing a convenient …