Results 21 to 30 of about 146,643 (141)

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
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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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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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Large graph clustering using DCT-based graph clustering [PDF]

2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD), 2014
With the proliferation of the World Wide Web, graph structures have arisen on social network/media sites. Such graphs usually number several million nodes, i.e., they can be characterized as Big Data. Graph clustering is an important analysis tool for other graph related tasks, such as compression, community discovery and recommendation systems, to ...
Tsapanos, Nikolaos, Tefas, Anastasios, Nikolaidis, Nikolaos, Pitas, Ioannis   +3 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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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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Parallel local graph clustering [PDF]

Proceedings of the VLDB Endowment, 2016
Graph clustering has many important applications in computing, but due to growing sizes of graph, even traditionally fast clustering methods such as spectral partitioning can be computationally expensive for real-world graphs of interest. Motivated partly by this, so-called local algorithms for graph clustering have received significant interest due to
Shun, Julian, Roosta-Khorasani, Farbod, Fountoulakis, Kimon, Mahoney, Michael W.   +3 more
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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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Dynamic-Fusion Multi-view Projection Clustering Algorithm [PDF]

Jisuanji kexue yu tansuo, 2023
Multi-view clustering is a hot research area, which has attracted increasing attention. Most existing multi- view clustering methods usually learn the data first, and then cluster the fused unified graph to get the final result. This two-step strategy of
JIANG Kaibin, ZHOU Shibing, QIAN Xuezhong, GUAN Jiaojiao
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Cluster Persistence for Weighted Graphs

Entropy, 2023
Persistent homology is a natural tool for probing the topological characteristics of weighted graphs, essentially focusing on their 0-dimensional homology. While this area has been thoroughly studied, we present a new approach to constructing a filtration for cluster analysis via persistent homology. The key advantages of the new filtration is that (a)
Omer Bobrowski, Primoz Skraba
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