The wide range of clustering applications includes search engines, social networks, visual tasks such as image segmentation, and DNA analysis. Search engines need to cluster information in order to be able to retrieve relevant data in the times of querying. Social networks inherently appear in a ...
Graph data models are useful to store, process and analyse highly interlinked data [1]. This is achieved through the use of graph theory to store data in the form of nodes and edges [2]. With the recent rise in the popularity of graph databases, which rely on graph data models to stor...
Hierarchical clustering is a common algorithm in data analysis. It is unique among several clustering algorithms as it draws dendrograms through a specific metric and extracts groups of features. It is widely used in all areas of astronomical research, covering systems at various scales, from astero...
In order to visualize the stability analysis, the boxplots are also plotted and shown in Fig. 4. From Fig. 4, it is detected that the stability of the KCGWO is better than all selected algorithms. Figure 3 Convergence curve obtained by all algorithms. Full size image Figure 4 Boxplot ...
The data consists of means and variances of the measures Fop and Fb of the methods PRcut, dPRcut, Ncut, dNcut. Figure titles at the top of each plot indicate the values being plotted along with the R2 values of the fit Full size image...
It measures the euclidean distance between each data point and its cluster center and chooses the number of clusters based on where change in “within cluster sum of squares” (WCSS) levels off. This value represents the total variance within each cluster that gets plotted against the number ...
Full size image All clustering methods exhibited performance that varied considerably across datasets. To reveal the effect of data complexity on performance, we plotted the average ARI by all methods for each slice as a function of data complexity. (Fig.5g–j). To quantify data complexity, we...
Download: Download full-size image Fig. 1. Illustration of a point cloud aggregated into ℓ=6 groups (dashed circles) and merged into k=3 clusters (labeled by color). The data points are ordered along the direction of the first principal component indicated by the arrow, and the six star...
Fig. 4: GraphST enables accurate vertical and horizontal integrations of ST data on mouse breast cancer and mouse brain anterior and posterior data, respectively. AFirst set of mouse breast cancer sample images aligned with the PASTE algorithm and plotted before batch effect correction.BUMAP plots...
The ccPGMM utilizes information that is available on some pixels and specifies constraints on those pixels belonging to the same or different clusters while clustering the rest of the pixels in the image. A latent variable model is used to represent the high-dimensional data in terms of a ...