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Multi-view Clustering: A Survey

Big Data Mining and Analytics, 2018
In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis ...
Yan Yang, Hao Wang
doaj   +2 more sources

Late Fusion Incomplete Multi-View Clustering. [PDF]

IEEE Trans Pattern Anal Mach Intell, 2019
Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel $k$k-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint optimization problem where the imputation and ...
Liu X, Zhu X, Li M, Wang L, Tang C, Yin J, Shen D, Wang H, Gao W.   +8 more
europepmc   +5 more sources

View-Driven Multi-View Clustering via Contrastive Double-Learning [PDF]

Entropy
Multi-view clustering requires simultaneous attention to both consistency and the diversity of information between views. Deep learning techniques have shown impressive abilities to learn complex features when working with extensive datasets; however ...
Shengcheng Liu, Changming Zhu, Zishi Li, Zhiyuan Yang, Wenjie Gu   +4 more
doaj   +2 more sources

Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus [PDF]

BMC Medical Genomics, 2017
Background The Cancer Genome Atlas (TCGA) has collected transcriptome, genome and epigenome information for over 20 cancers from thousands of patients. The availability of these diverse data types makes it necessary to combine these data to capture the ...
Menglan Cai, Limin Li
doaj   +2 more sources

Multi-view clustering via global-view graph learning [PDF]

PLoS ONE
Qin Li, Geng Yang
doaj   +2 more sources

Smoothed Multi-view Subspace Clustering [PDF]

, 2021
In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not obtain the optimal solution.
Chen, Peng, Liu, Liang, Ma, Zhengrui, Kang, Zhao   +3 more
openaire   +2 more sources

Unsupervised Multi-View K-Means Clustering Algorithm

IEEE Access, 2023
Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
doaj   +1 more source

An improved multi-view spectral clustering based on tissue-like P systems

Scientific Reports, 2022
Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view.
Huijian Chen, Xiyu Liu
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FMvC: Fast Multi-View Clustering

IEEE Access, 2023
In multi-view clustering, an eigen-decomposition of the Laplacian matrix of the graph is usually necessary. This leads to a significant increase in time cost and also requires post-processing such as $k$ -means.
Jiada Wang, Yijun Liu, Wujian Ye
doaj   +1 more source

Multi-View Fuzzy Clustering Algorithm Fused with KL Information [PDF]

Jisuanji gongcheng, 2022
Existing multi-view Fuzzy C-Means(FCM) clustering algorithms usually artificially decompose multi-view data into multiple single-view data for processing, reducing the clustering accuracy of view data and affecting the results of global data division.To ...
HE Na, MA Yingcang
doaj   +1 more source