Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …

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 …

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 …

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 …

Motivation: In recent years, work has been carried out on clustering gene expression
microarray data. Some approaches are developed from an algorithmic viewpoint whereas …

The road traffic safety situation is severe worldwide and exploring driving behavior is a
research hotspot since it is the main factor causing road accidents. However, there are few …

The last decade has seen an explosion of work on the use of mixture models for clustering.
The use of the Gaussian mixture model has been common practice, with constraints …

In this paper we describe an algorithm for clustering multivariate time series with variables
taking both categorical and continuous values. Time series of this type are frequent in health …

This paper presents the R package HDclassif which is devoted to the clustering and the
discriminant analysis of high-dimensional data. The classification methods proposed in the …

Clustering of longitudinal data is becoming increasingly popular in many fields such as
social sciences, business, environmental science, medicine and healthcare. However, it is …