Model-Based Clustering and Classification for Data Science: With Applications in RNo abstract is available for this item.doi:10.1080/00031305.2020.1745576Seung Jun ShinTaylor & Francis JournalsThe American Statistician
Siegmund et al. (2004) argue that model-based clustering is preferred in this context over hierarchical clustering [13], a finding that bears out in our simulations. One reason for the superior performance, at least in a high-dimensional context, is that the metric used to characterize the d...
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-
我们将我们的结果与ICDAR竞赛的最高参与者以及最近的深度学习方法进行了比较(表1)。尽管PhotoOCR使用了790万个训练图像,但RCN模型的表现优于顶级竞争者PhotoOCR 1.9%,而RCN使用了来自25,584个字体图像的基于模型的聚类(model-based clustering)选择的1,406个训练图像。除了提供竞争方法无法提供的字符的详细分段(图7E)...
The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to thesame distribution. A convenient property of this approach is that this closely resembles the way artificial data sets are generated: by...
mixMVPLN is an R package for performing model-based clustering of three-way count data using finite mixtures of matrix variate Poisson-log normal (mixMVPLN) distributions (Silva et al., 2023). Three different frameworks are available for parameter estimation of the mixMVPLN models:...
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. ...
Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. Howeve
To infer population structure in this broad sample, we used a model-based clustering algorithm implemented in the computer program Structure version 2 [8, 9]. This algorithm uses multilocus genotype to identify a predetermined number (K) of clusters that have distinctive allele frequencies and assig...
A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic...