S. Banerjee and A. Rosenfeld: Model-based cluster analysis. Pattern Recognition , 26: 963–974, 1993.Model-based cluster analysis - Banerjee, Rosenfeld - 1993 () Citation Context ...n mixture modeling. Moreover,
For this data, it can be seen that model-based clustering selected a model with three components (i.e. clusters). The optimal selected model name is VVV model. That is the three components are ellipsoidal with varying volume, shape, and orientation. The summary contains also the clustering t...
One important facet of network analysis that receives significant attention is community detection. 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 ...
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-
In Section 2, we discuss the methodology for clustering directed weighted networks. In Section 3, we illustrate the effectiveness of the proposed procedure on synthetic data. Section 4 provides the analysis of the trades data set in unilayer and multilayer cases. Finally, the paper is concluded ...
graph theory-based clustering analysis(GTBCA), is proposed, which illustrates the construction of platform through analyzing the commonality and standardization on a set of existing similar products of a company. 提出了一个平台体系结构的分层构造框架及一种平台元素的获取方式———基于图论的聚类分析方法...
(2008). Model-based clustering of multiple time series. Journal of Business & Economic Statistics 26, 78-89.Fruhwirth-Schnatter, S., and Kaufmann, S. (2008). "Model-based clustering of mul- tiple time series." Journal of Business and Economic Statistics, 26, 78-89....
Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. 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...
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 ...
analysis problem which poses challenges for traditional clustering approaches. In addition, analysis of heterogeneous tissue data can lead to a large number of clusters, as we demonstrate below, which presents further challenges for clustering techniques. For example, nonparametric approaches rely on a ...