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....
clustering, discriminant analysis, and density estimation on six real datasets from flow cytometry, finance, and image analysis. We also compare the performance of mixtures of skew normal andt-component distributions with other non-normal component distributions, including mixtures with multivariate normal...
Clustering is a critical step in single cell-based studies. Most existing methods support unsupervised clustering without the a priori exploitation of any domain knowledge. When confronted by the high dimensionality and pervasive dropout events of scRNA-
This is mainly due to the fact that model-based clustering methods are dramatically over-parametrized in this case. However, high-dimensional spaces have specific characteristics which are useful for clustering and recent techniques exploit those characteristics. After having recalled the bases of model...
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...
However, as far as it is concerned, the clustering approach was seldom hybrid with machine learning models for building energy demands prediction. Only Chen et al. [28] proposed a hybrid support vector regression model to predict the day-ahead hourly electric demand based on feature identification...
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 relationsh
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: ...
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...