Results 31 to 40 of about 86,218 (261)
A Feature-Reduction Multi-View k-Means Clustering Algorithm
IEEE Access, 2019 The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas.
Miin-Shen Yang, Kristina P. Sinaga
doaj +1 more source
Kernel-Induced Incomplete Multi-view Clustering
Jisuanji kexue yu tansuo, 2021 With the development of technology, data often have multiple forms which come from multiple sources. The multi-view clustering algorithm aims to use the complementary information existing in different sources for clustering.
ZHANG Wei, DENG Zhaohong, WANG Shitong
doaj +1 more source
Diversity-induced Multi-view Subspace Clustering Algorithm with Grouping Effect [PDF]
Jisuanji gongchengThe multi-view subspace clustering algorithm, a type of multi-view clustering algorithm, emphasizes discovering potential subspaces in multi-view data and clustering based on these subspaces.
ZHANG Yuechen, GE Hongwei, LI Ting
doaj +1 more source
IEEE Access, 2019 When processing a multi-view, epilepsy electroencephalogram (EEG) dataset, the traditional single-view clustering algorithms cannot fully mine the correlation information between each view and identify the importance of each view because of the ...
Jiaqi Zhu +7 more
doaj +1 more source
Multi-view clustering via consensus coefficient matrix and separate segmentation matrices
Journal of Information and TelecommunicationIn recent years, achieving data from different sources and different views has caused to have many multi-view data sets. Among multi-view learning methods, multi-view clustering has been considered as an appropriate method to analyse these data by many ...
Fatemeh Sadjadi +2 more
doaj +1 more source
Interpretable multi-view clustering
Pattern RecognitionMulti-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains
Mudi Jiang +3 more
openaire +2 more sources
Generative Partial Multi-View Clustering
CoRR, 2020 This paper is an extension to our previous work: "Wang Q, Ding Z, Tao Z, et al. Partial multi-view clustering via consistent GAN[C]//2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018: 1290-1295."
Qianqian Wang 0001 +4 more
openaire +2 more sources
Fairness-aware Multi-view Clustering
, 2023 In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), the heterogeneous data may include not only numerical data (e.g., total debt and yearly income), but also text and images (e.g., financial statement and invoice images).
Lecheng Zheng, Yada Zhu, Jingrui He
openaire +2 more sources
Multi-view Spectral Clustering Network [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Multi-view clustering aims to cluster data from diverse sources or domains, which has drawn considerable attention in recent years. In this paper, we propose a novel multi-view clustering method named multi-view spectral clustering network (MvSCN) which could be the first deep version of multi-view spectral clustering to the best of our knowledge.
Zhenyu Huang 0005 +5 more
openaire +1 more source
AMCFCN: attentive multi-view contrastive fusion clustering net [PDF]
PeerJ Computer ScienceAdvances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets.
Huarun Xiao +3 more
doaj +2 more sources