The invention discloses a method for optimizing of model selection based on clustering analysis. The method comprises the following steps of 1, establishing a plurality of three-dimensional quantitative geological models, calculating the attribute value of each grid node of each model, standardizing ...
Model-based clustering is a statistical approach to data clustering. The observed (multivariate) data is assumed to have been generated from a finite mixture of component models. Each component model is a probability distribution, typically a parametric multivariate distribution. For example, in a mul...
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...
‘false’ zero count observations. Here, we have developed scDeepCluster, a single-cell model-based deep embedded clustering method, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Based on testing extensive simulated data and real ...
In this article, we introduce a clustering-based method that can generate commendable topics as long as high-quality contextualized embeddings are used, together with an appropriate topic word selecting method. Compared to neural topic models, clustering-based models are more simple, efficient and rob...
For the cluster-based pattern recognition, outliers are usually regarded as “harmful”, the main measures taken here are to minimize or eliminate this harm and the outliers are detected as the by-products of the clustering. While for the outlier mining, the outlier itself becomes the focus. ...
Yu and Abdel-Aty (2014) applied the fixed parameter logistic model, the support vector machine (SVM), and the random parameter logit model in predicting injury severity on a mountainous freeway in real time. Sun and Sun (2016) proposed a model based on clustering algorithm and SVM to ...
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...
This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outpu...
In this Review, we first provide a brief introduction to key single-cell and whole-tissue approaches that allow investigation of tissue specificity and then highlight two classes of machine-learning-based methods, which can be applied to analyse, model and interpret these experimental data. Deep ...