Model-Based Clustering Methods for Time SeriesThis paper considers the problem of clustering n observed time series $$\\mathbf{x}_{k} =\\{\\ x_{k}(t)\\ \\vert \\ t \\in \\mathHansHermann Bock
He is currently a Professor at the University of Alabama. He also serves on the Board of Directors of Classification Society of North America. His main research interests include model based clustering methods, clustering high-dimensional objects, and data visualization....
model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each ... C Fraley,A Raftery - 《Journal of Statistical Software》 被引量: 343发表: 2007年 A comparison of model-based and regression classification ...
Model-based co-clustering for mixed type data 2020, Computational Statistics and Data Analysis Show abstract Review of Clustering Methods for Functional Data 2023, ACM Transactions on Knowledge Discovery from Data Optimized Density Peaks Clustering Algorithm Based on Dissimilarity Measure 2020, Ruan Jian ...
Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in pat...
Multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory; Covariates and distal outcomes; Parametric and non-parametric bootstrapping. Reference If you find StepMix useful, please leave a ⭐ and consider citing ourarXiv preprint: ...
EMCluster is an R package providing EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.InstallationEMCluster requires...
Model-based clustering is a popular technique relying on the notion of finite mixture models that proved to be efficient in modeling heterogeneity in data. The underlying idea is to model each data group by a particular mixture component. This relationship between mixed distributions and clusters for...
Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. Howeve
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in ...