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 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...
Clustering multivariate functional data in group-specific functional subspaces Model-based clustering of time series in group-specific functional subspaces. Bouveyron, Charles,Jacques, Julien. Advances in Data Analysis and Classification . 2011Schmutz, A.; Jacques, J.; Bouveyron, C.; Cheze, L.; ...
especially in predictive maintenance. Among the categories of clustering techniques, the partition-based, the distribution-based, hierarchy-based and the neural network-based are widely
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-
The subpopulations are not defined a priori but are determined on the basis of similarities in behavior in order to determine which women exhibit similar characteristics with respect to method choice, method switch, discontinuation and subsequent resumption of contraceptive use. The data are from the ...
Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models To illustrate the practical merits of the proposed method, we consider its applications in model-based clustering and mixture discriminant analysis. Numerical ... L Ruan,M Yuan,H Zou - 《Neural Computation》 被引量: 29发表...
‘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 ...
摘要: This paper presents a detailed empirical study of 12 generative approaches to text clustering, obtained by applying four types of document-to-cluster assignment strategies (hard, stochastic, soft a...关键词: Comparative study Document clustering Model-based clustering ...
Explore all metrics Abstract This article proposes a new fuzzy time series (NFTS) model that can interpolate historical data to forecast effectively for the future. In this model, after normalizing original data, we establish the automatic algorithm to determine the suitable number of clusters and ...