The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

Cluster analysis is reformulated as a problem of estimating the para- meters of a mixture of multivariate distributions. The maximum-likelihood theory and numerical solution techniques are developed for a fairly general class of distributions. The theory is applied to mixtures of multivariate nor- mals (NORMIX) and mixtures of multivariate Bernoulli distributions (Latent Classes). The feasibili...

Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it a...

Identifying directions where extreme events occur is a significant challenge in multivariate value analysis. In this article, we use the concept of sparse regular variation introduced by Meyer and Wintenberger to infer tail dependence random vector X. This approach relies on Euclidean projection onto simplex which better exhibits sparsity structure X than standard methods. Our procedure based r...

The effects of five towns on river water pollution were examined along the Łyna River (southern watershed of the Baltic Sea, northern Poland). The relationships among the spatially derived indicators of urbanization, environmental variables, and physico-chemical and microbiological data (heterotrophic plate count at 22 and 37 °C, and fecal coli) obtained from longitudinal river profiling have b...

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations ...

x-ray absorption fine structure (xafs) spectroscopy was used to identify directly the metal speciation and local bonding environment of fe in sediments originated from dobczyce reservoir (poland); special attention was paid to analysis of samples collected from points situated at different distances from the land. the combination of traditional approach to data analysis as well as usage of mult...