Results 31 to 40 of about 156,516 (263)

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
doaj   +1 more source

Memetic Graph Clustering

CoRR, 2018
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions.
Sonja Biedermann, Monika Henzinger, Christian Schulz 0003, Bernhard Schuster   +3 more
openaire   +5 more sources

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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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
doaj   +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
doaj   +1 more source

Cluster-Guided Contrastive Graph Clustering Network

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1)
Xihong Yang, Yue Liu 0008, Sihang Zhou 0001, Siwei Wang 0001, Wenxuan Tu, Qun Zheng, Xinwang Liu 0002, Liming Fang 0001, En Zhu   +8 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
doaj   +1 more source

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
openaire   +5 more sources

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
doaj   +1 more source

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 ...
Nikolaos Tsapanos, Anastasios Tefas, Nikolaos Nikolaidis 0001, Ioannis Pitas   +3 more
openaire   +3 more sources