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 ...
We provide a theoretical analysis using standard notions of graph approximation, significantly generalizing previous results (which focused on spectral clustering with two clusters). We also propose a new algorithmic approach based on adaptive sampling, which experimentally matches or improves on previous ...
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...
A second group lists methods depending on the cluster model acquired such as hierarchical [4], centroid (as in K-means [5]), distribution such as expectation maximization [6], density [7,8], subspace, group, and graph-based models [9,10]. Thirdly, depending on the relationship type ...
a Illustration of Forest Fire Clustering. In the data-preprocessing stage, a KNN data graph is created by transforming the pairwise distances into affinities using adaptive kernels (Steps 1–2). Then, a vertex is randomly selected as the seed to take on a label (Step 3). The label is al...
There are no details provided in the study on how to optimally chose the user defined value K. Moreover, the merging phase of KMDD requires a manual selection of G core cluster centers in the decision graph. BIRCH uses a Clustering Feature (CF) tree to obtain an initial result and then...
2023Dual Fusion-Propagation Graph Neural Network for Multi-view ClusteringDFP-GNNTMM- 2023Joint Shared-and-Specific Information for Deep Multi-View ClusteringJSSITCSVT- 2023On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view ClusteringDeepMVCCVPR ...
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...
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...