Results 41 to 50 of about 146,643 (141)

Multi-View Enhancement Graph-Level Clustering Network

IEEE Access
Graph-level clustering is a fundamental and significant task in data mining. The advancement of graph neural networks has provided substantial impetus to this area of research.
Zeyi Li, Renda Han, Tianyu Hu, Mengfei Li, Caimao Li   +4 more
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One-step Multi-view Clustering Based on Diversity and Consistency [PDF]

Jisuanji gongcheng
With the development of data collection technology, multi-view data have become increasingly common. Compared to single-view data, multi-view data contain richer information, which is usually characterized by consistency and diversity information.
HU Aoran, CHEN Xiaohong
doaj   +1 more source

FDC-LGL: Fast Discrete Clustering with Local Graph Learning for Large-Scale Datasets

Mathematics
Graph-based clustering is a fundamental task in unsupervised machine learning and has been extensively applied to complex data mining scenarios, such as pattern recognition and data classification. However, most existing graph clustering algorithms still
Shenfei Pei, Ruiyu Huang, Zengwei Zheng
doaj   +1 more source

Clustering graph data: the roadmap to spectral techniques

Discover Artificial Intelligence
Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance.
Rahul Mondal, Evelina Ignatova, Daniel Walke, David Broneske, Gunter Saake, Robert Heyer   +5 more
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Novel Algorithms for Graph Clustering Applied to Human Activities

Mathematics, 2021
In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented ...
Nebojsa Budimirovic, Nebojsa Bacanin
doaj   +1 more source

A Scalable Deep Network for Graph Clustering via Personalized PageRank

Applied Sciences, 2022
Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1)
Yulin Zhao, Xunkai Li, Yinlin Zhu, Jin Li, Shuo Wang, Bin Jiang   +5 more
doaj   +1 more source

One-Step Graph Fusion Fuzzy Clustering Network

IEEE Access
Graph clustering plays a crucial role in uncovering implicit information within data which can be used to rationally classify potential data samples in unsupervised scenarios.
Bin Tang, Zhongyang Zhou, Xuewen Liu, Feiyu Chen   +3 more
doaj   +1 more source

Completely connected clustered graphs

Journal of Discrete Algorithms, 2003
A clustered graph \((G,T,r)\) consists of a graph \(G=(V,E)\), a tree \(T\), and an inner vertex \(r\) of \(T\) such that the set of leaves of \(T\) is exactly \(V\). A clustered graph is said to be completely connected if every cluster, and also each complement of a cluster, induces a connected subgraph.
Cornelsen, Sabine, Wagner, Dorothea
openaire   +2 more sources

Fast Spectral Clustering via Efficient Multilayer Anchor Graph

International Journal of Aerospace Engineering
Recent studies have shown that graph-based clustering methods are good at processing hyperspectral images (HSIs), while falling short for large-scale HSIs due to high time complexity.
Yiwei Wei, Chao Niu, Dejun Liu, Peinan Ren   +3 more
doaj   +1 more source

Graph Convolution-Based Deep Clustering for Speech Separation

IEEE Access, 2020
Deep clustering is a promising technique for speech separation that is crucial to speech communication, acoustic target detection, acoustic enhancement and speech recognition. In the study of monophonic speech separation, the problem is that the decrease
Shan Qin, Ting Jiang, Sheng Wu, Ning Wang, Xinran Zhao   +4 more
doaj   +1 more source