Model-based clustering
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 …
where the number of groups is unknown. Through its basis in a statistical modeling …
A novel approach for Gaussian mixture model clustering based on soft computing method
M Gogebakan - IEEE Access, 2021 - ieeexplore.ieee.org
Determining the number of clusters in a data set is a significant and difficult problem in
cluster analysis. In this study, a new model-based clustering approach is proposed for the …
cluster analysis. In this study, a new model-based clustering approach is proposed for the …
Multiple change point clustering of count processes with application to California COVID data
In this paper, a model-based clustering algorithm relying on a finite mixture of negative
binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic …
binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic …
Longitudinal data clustering methods: A Systematic Review
In the last few decades, in many research fields, different methods were introduced to
discover groups with the same trends in longitudinal data. The clustering process is an …
discover groups with the same trends in longitudinal data. The clustering process is an …
Longitudinal Data Clustering Methods: A Systematic ReviewLongitudinal Data Clustering Methods: A Systematic Review
Introduction: In the last few decades, in many research fields, different methods were
introduced to discover groups with the same trends in longitudinal data. The clustering …
introduced to discover groups with the same trends in longitudinal data. The clustering …