Results 31 to 40 of about 146,643 (141)
PLoS Computational Biology, 2022 Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited ...
Snehalika Lall +2 more
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Graph clustering method based on structure entropy constraints
网络与信息安全学报, 2021 Aiming at the problem of how to decode the true structure of the network from the network embedded in the large-scale noise structure at the open information sharing platform centered on big data, and furthermore accurate mining results can be obtained ...
ZHANG Zhiying, TIAN Youliang
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Cluster graph modification problems [PDF]
Discrete Applied Mathematics, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shamir, Ron, Sharan, Roded, Tsur, Dekel
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Sampling clustering based on multi-view attribute structural relations.
PLoS ONEIn light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes.
Guoyang Tang +3 more
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Three-Way Decision-Driven Adaptive Graph Convolution for Deep Clustering
Applied SciencesGraph clustering is an efficient method for deep clustering that utilizes graph convolution. Graph convolution effectively combines structure and content information, and lots of recent graph convolution-based methods have shown promising results in ...
Wei Liang +4 more
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A parameter-free graph reduction for spectral clustering and SpectralNet
Array, 2022 Graph-based clustering methods like spectral clustering and SpectralNet are very efficient in detecting clusters of non-convex shapes. Unlike the popular k-means, graph-based clustering methods do not assume that each cluster has a single mean.
Mashaan Alshammari +2 more
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Generalized Graph Clustering: Recognizing (p,q)-Cluster Graphs [PDF]
, 2010 CLUSTER EDITING is a classical graph theoretic approach to tackle the problem of data set clustering: it consists of modifying a similarity graph into a disjoint union of cliques, i.e, clusters. As pointed out in a number of recent papers, the cluster editing model is too rigid to capture common features of real data sets.
Pinar Heggernes +4 more
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A Short Text Clustering Algorithm Based on Spectral Cut [PDF]
Jisuanji gongcheng, 2016 Short text has the characteristics of sparsity and high dimension,and the existing clustering algorithm for the large-scale short text has low accuracy and efficiency.Aiming at this problem,a novel clustering method based on spectral clustering theory ...
LI Xiaohong,XIE Meng,MA Huifang,HE Tingnian
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Partitioning Well-Clustered Graphs: Spectral Clustering Works! [PDF]
SIAM Journal on Computing, 2017 In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix, and (2) grouping the embedded points into k clusters via k-means algorithms. We show that, for a wide class of graphs,
Zanetti, Luca, Sun, He, Peng, Richard
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Data & Knowledge Engineering, 2022 In application domains ranging from social networks to e-commerce, it is important to cluster users with respect to both their relationships (e.g., friendship or trust) and their actions (e.g., visited locations or rated products). Motivated by these applications, we introduce here the task of clustering the nodes of a sequence graph, i.e., a graph ...
Zhong, Haodi +2 more
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