clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational ….e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of …

small number of replications per variable, and HDLLSS data refer to HDLSS data observed over time. Clustering technique …, mass spectrometry data, pattern recognition. Most current clustering algorithms for HDLSS and HDLLSS data are adaptations … addition, available algorithms often exhibit poor clustering accuracy and stability for non-normal data. Simulations show that …

, data mining method clustering was undertaken to identify the customersegments. Two different clustering methods were tested …

model (GPPM) in which the partition process is predictor-dependent. The GPPM generalizes DP clustering to relax the …

dissertation, we propose a robust clustering method based on the EM algorithm applied to a modified multivariate normal density …

This paper investigates innovative activities, clustering and regional interaction of a group of Information and …

-means clustering were simulated within the the Farm-level Economic Model (FEM) to determine the economic impacts of four manure …