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....
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Semhar Michael& Igor Melnykov 3294Accesses Abstract 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 rel...
We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also...
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
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: ...
The traditional time aggregation methods mentioned beforeperform clustering based on the similarity information of the time series input data only. However, it isneglectedin the studies thatwhether similar data profiles have similar impacts on the ESOM results,as the ESOM is usually a non-linear mapp...
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 ...