We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we ...
Graph Agglomerative Clustering (GAC) toolbox GACluster library supports large datasets, and provides demo scripts for reproducing the state-of-the-art benchmark results. Introduction Gactoolbox is a summary of our research of agglomerative clustering on a graph. Agglomerative clustering, which iterative...
Such attributes contain extremely useful information and have the potential to improve the clustering process, but are neglected by most recent graph stream mining techniques. In this paper, we define a unified distance measure on both link structures and side attributes for clustering. In addition,...
The data graph can be approximated with a KNN graph, and an adaptive kernel can be used to accurately capture both the local structure and the long-range interactions between data points. 2. Label propagation: On the data graph, a random unlabeled vertex r is selected as a seed to take ...
transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category...
Also, you might find that users frequently exit the site after clicking on a particular product. Given that finding, you might ask what additional paths you could provide to users that would induce users to stay on the Web site. If you do not have additional information to use in ...
There are also other ways in which constraints can help in clustering: for example, one can use them to find clusterings that score better on a particular unsupervised optimization objective (e.g. they can help to obtain a lower with-cluster sum of squares (Ashtiani et al. 2016)). This,...
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large-scale datasets. In this work, we present DeepCluster, a clustering method that...
Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning. Nat. Commun. 13, 5962 (2022). Moran, P. A. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950). Article CAS PubMed Google Scholar Geary, R. C. The ...
Clustering is performed on the merged similarity matrix by using graph-based clustering algorithms such as spectral22 and Louvain algorithm16. However, similarity matrix-based clustering cannot explicitly consider the dropout events in scRNA-seq data. Hao et al. developed a weighted nearest-neighbor (...