Clustering AnalysisMathematicsParametricFinite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many clusters are there in the data and ...
The application of finite mixture models to cluster analysis, also known as model-based clustering [4], shows remarkable modeling flexibility and ability to produce excellent clustering results. Finite mixture model is a convex combination of several probability distributions, also known as mixture compo...
Model-based clustering is a statistical approach to data clustering. The observed (multivariate) data is considered to have been created from a finite combination of component models. Each component model is a probability distribution, generally a parametric multivariate distribution. For instance, in ...
However, while most community detection algorithms focus on clustering the actors of the network, it is very intuitive to cluster the edges. Connections exist because they were formed within some latent environment such as, in the case of a social network, a workplace or religious group, and ...
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-
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...
We propose a novel method for model-based clustering of data of the type produced by Illumina GoldenGate arrays. Our method makes use of a beta mixture model [15]. Although one could use BIC (or similar quantities) to select the number of clusters in the data set, we propose a recursive...
However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing 鈥榝alse鈥?zero count observations. Here, we have developed scDeepCluster, a single-cell model-based deep embedded clustering...
In addition to be the first co-clustering algorithm for functional data, the advantage of the proposed model is its ability to extract the hidden double structure induced by the data and its ability to deal with missing values. The model has proven its efficiency on simulated data and on a ...
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...