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
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...
Experiments on simulated and real datasets highlight the good performance of the proposed approach as compared to existing clustering methods while providing a useful representation of the cluster 展开 关键词: High-dimensional clustering – Model-based clustering – Discriminative subspace – Fisher ...
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-
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...
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. Installation EMCluster requires R version 3.0.0 or higher. R...
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 ...
Dimensionality reduction and clustering are representative UL techniques. As depicted in Table 1, these three types of methods exhibit significant differences across all three dimensions: data type used, feedback mechanism for the result, and target. Table 1. Differences between ERL, SL, UL. ...
After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision...
StepMix can be used for both clustering and supervised learning. Additional features include: Support for missing values through Full Information Maximum Likelihood (FIML); Multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory; Covariates and distal outcomes; ...