Results 1 to 10 of about 1,500,611 (328)
Constrained parsimonious model-based clustering. [PDF]
Stat Comput, 2022 AbstractA new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases.
García-Escudero LA +2 more
europepmc +7 more sources
clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R [PDF]
Journal of Statistical Software, 2018 Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mixture models. Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering ...
Luca Scrucca, Adrian E. Raftery
doaj +2 more sources
Parametric model-based clustering [PDF]
SPIE Proceedings, 2005 Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step}, and will recompute parametric models using data specifically from the corresponding clusters ...
Nikulin, Vladimir, Smola, Alex
core +5 more sources
Latent Model-Based Clustering for Biological Discovery [PDF]
iScience, 2019 Summary: LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters.
Xin Bing +3 more
doaj +2 more sources
MODEL-BASED CLUSTERING OF LARGE NETWORKS. [PDF]
Ann Appl Stat, 2013 We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation ...
Vu DQ, Hunter DR, Schweinberger M.
europepmc +6 more sources
Detecting Differentially Variable MicroRNAs via Model-Based Clustering [PDF]
International Journal of Genomics, 2018 Identifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in statistics.
Xuan Li +6 more
doaj +2 more sources
Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior
Stats, 2022 We discuss a Bayesian hierarchical copula model for clusters of financial time series. A similar approach has been developed in recent paper. However, the prior distributions proposed there do not always provide a proper posterior. In order to circumvent
Paolo Onorati, Brunero Liseo
doaj +1 more source
Journal of Statistical Software, 2021 We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework.
Sergio Venturini, Raffaella Piccarreta
doaj +1 more source
Journal of Data Science, Statistics, and Visualisation, 2022 We propose a class of Fisher-consistent robust estimators for mixture models. These estimators are then used to build a robust model-based clustering procedure. We study in detail the case of multivariate Gaussian mixtures and propose an algorithm, similar to the EM algorithm, to compute the proposed estimators and build the robust clusters.
Juan Domingo Gonzalez +3 more
openaire +2 more sources
Annual Review of Statistics and Its Application, 2023 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 framework, model-based clustering provides a principled and reproducible approach to clustering.
Isobel Claire Gormley +2 more
+4 more sources