Results 21 to 30 of about 1,256,700 (250)

Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion [PDF]

Jisuanji kexue
Multi-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of
WANG Jinfu, WANG Siwei, LIANG Weixuan, YU Shengju, ZHU En
doaj   +1 more source

A Deep Graph Structured Clustering Network

IEEE Access, 2020
Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information
Xunkai Li, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, Meixia Qu   +5 more
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Fine-grained Attributed Graph Clustering

, 2022
Graph clustering is a prevalent issue associated with social networks, data mining, and machine learning; its objective is to detect communities or groups in networks.
Kang, Z, Liu, Z, Tian, L, Pan, S, Shirui Pan, Zhao Kang, Zhanyu Liu, Ling Tian   +7 more
core   +1 more source

Adaptive Graph Representation for Clustering

IEEE Access, 2022
Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen, Youshuai Wang, Yongxu Chen, Hongyu Zhu, Yue Xie, Pengju Guo   +5 more
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Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection

Entropy, 2023
In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect ...
Ni Li, Manman Peng, Qiang Wu
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Spectral clustering based on high‐frequency texture components for face datasets

IET Image Processing, 2021
Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data.
Zexiao Liang, Shaozhi Guo, Dakang Liu, Jianzhong Li   +3 more
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Graph Embedding via Graph Summarization

IEEE Access, 2021
Graph representation learning aims to represent the structural and semantic information of graph objects as dense real value vectors in low dimensional space by machine learning.
Jingyanning Yang, Jinguo You, Xiaorong Wan   +2 more
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SACOC: A spectral-based ACO clustering algorithm [PDF]

, 2014
The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
Otero, Fernando E. B., David Camacho, Héctor D. Menéndez, Otero, Fernando E.B., Camacho, David, Menendez, Hector, Menéndez, Héctor D., Fernando E. B. Otero   +7 more
core   +1 more source

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

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, Sumanta Ray, Sanghamitra Bandyopadhyay   +2 more
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Three-Way Decision-Driven Adaptive Graph Convolution for Deep Clustering

Applied Sciences
Graph 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, Dong Li, Chuanpeng Wang, Kai Chen, Suijie Song   +4 more
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