Clustering procedures allowing for general covariance structures of the obtained clusters need some constraints on the … restriction on the “eigenvalues-ratio” of the clusters scatter matrices. In order to try to achieve robustness with respect to …

solutions to this problem and are effective in higher dimensions. We use mixture model-based clustering applications to …

Gaussian and t-distribution analogues, mixtures of skew-t factor analyzers are very well-suited for model-based clustering of … data, giving superior clustering results when compared to a well-established family of Gaussian mixture models. …

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random … posteriori criterion to choose between two discordant clustering algorithm is presented. …

A new algorithm for clustering life course trajectories is presented and tested with large register data. Life courses … clustering result for this kind of data provides interesting subgroups with similar life course trajectories. The high sampling … information. The new algorithm deals with this problem by simultaneously clustering and identifying critical junctures for each of …

Expectation Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces … clustering algorithms. This paper presents an algorithm for the novel hybridization of EM and K-Means techniques for achieving … better clustering performance (NovHbEMKM). This algorithm first performs K-Means and then using these results it performs EM …

authors propose a Mobility Aware Clustering Scheme (MACS), which organizes the IoT devices in clusters using a fitness …

one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm …