└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # MVC4Applications 2 | This repository lists related work using MVC methods for applications. We hope to investigate the special issues in application scenarios of multi-view clustering analysis, to achieve the future improvements on MVC algorithms for this community, so feel free to contact me in this repository for updating (recommended paper, datasource, code...). 3 | 4 | ## Medical data analysis 5 | 6 | DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data, **[Zou et al.](https://doi.org/10.1093/bib/bbac347)** 7 | 8 | scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration, **[Li et al.](https://www.nature.com/articles/s41467-023-41795-5)** 9 | 10 | Biotypes of major depressive disorder identified by a multiview clustering framework, **[Chen et al.](https://www.sciencedirect.com/science/article/pii/S0165032723002860)** 11 | 12 | Multi-omics clustering based on dual contrastive learning for cancer subtype identification, **[Chen et al.](https://dl.acm.org/doi/abs/10.1145/3570773.3570883)** 13 | 14 | Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification, **[Madhumita et al.](https://www.nature.com/articles/s41598-022-17585-2)** 15 | 16 | A multiobjective multi-view cluster ensemble technique: Application in patient subclassification, **[Mitra et al.](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216904)** 17 | 18 | Multi-omic and multi-view clustering algorithms: review and cancer benchmark, **[Rappoport et al.](https://academic.oup.com/nar/article/46/20/10546/5123392)** 19 | 20 | Consensus clustering applied to multi-omics disease subtyping, **[Brière et al.](https://link.springer.com/article/10.1186/s12859-021-04279-1)** 21 | 22 | A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data, **[Mo et al.](https://academic.oup.com/biostatistics/article/19/1/71/3852318)** 23 | 24 | Integrate multi-omic data using affinity network fusion (ANF) for cancer patient clustering, **[Ma et al.](https://ieeexplore.ieee.org/abstract/document/8217682)** 25 | 26 | Multi-View Clustering of Clinical Documents Based on Conditions and Medical Responses of Patients, **[Sabthami et al.](https://ieeexplore.ieee.org/abstract/document/7726951)** 27 | 28 | CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression, **[Zhang et al.](https://www.sciencedirect.com/science/article/pii/S0169260720317284?casa_token=vL8d7d6nx4wAAAAA:jUH-FeOLCDJ_PqGmWZ56pVzim1FoCioMC5qdYYh61vIVE6l8D8OHf68W-FbMRQWk2fDv28aEqA)** 29 | 30 | Protein complex detection based on partially shared multi-view clustering, **[Ou-Yang et al.](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1164-9)** 31 | 32 | Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction, **[Liu et al.](https://www.frontiersin.org/articles/10.3389/fgene.2021.718915/full)** 33 | 34 | Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping, **[Ge et al.](https://academic.oup.com/bib/article/24/1/bbac500/6850565)** 35 | 36 | MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites, **[Zuo et al.](https://academic.oup.com/bib/article/23/5/bbac277/6661182)** 37 | 38 | Research on children's respiratory diseases based on partition level multi-view clustering, **[Zhang et al.](https://doi.org/10.1117/12.2687644)** 39 | 40 | Multi-graph Clustering Based on Interior-Node Topology with Applications to Brain Networks, **[Ma et al.](https://link.springer.com/chapter/10.1007/978-3-319-46128-1_30)** 41 | 42 | Clustering of single-cell multi-omics data with a multimodal deep learning method, **[Lin et al.](https://www.nature.com/articles/s41467-022-35031-9)** 43 | 44 | ## Internet data analysis 45 | 46 | Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations, **[Liu et al.](https://dl.acm.org/doi/pdf/10.1145/3534678.3539105)** 47 | 48 | Comment-based Multi-View Clustering of Web 2.0 Items, **[He et al.](https://dl.acm.org/doi/abs/10.1145/2566486.2567975)** 49 | 50 | Social web video clustering based on multi-view clustering via nonnegative matrix factorization, **[Mekthanavanh et al.](https://link.springer.com/article/10.1007/s13042-018-00902-5)** 51 | 52 | Web Items Recommendation Based on Multi-View Clustering, **[Yu et al.](https://ieeexplore.ieee.org/abstract/document/8377689)** 53 | 54 | Multi-View Clustering of Web Documents using Multi-Objective Genetic Algorithm, **[Wahid et al.](https://ieeexplore.ieee.org/abstract/document/6900586)** 55 | 56 | Multi-view clustering for mining heterogeneous social network data, **[Greene et al.](https://www.researchgate.net/profile/Derek-Greene/publication/222089360_Multi-view_clustering_for_mining_heterogeneous_social_network_data/links/09e41510e75e85bf3d000000/Multi-view-clustering-for-mining-heterogeneous-social-network-data.pdf)** 57 | 58 | A robust multi-view clustering method for community detection combining link and content information, **[He et al.](https://www.sciencedirect.com/science/article/pii/S0378437118312184?casa_token=cTuxJjYlkBAAAAAA:qtd4VrAS0PXxlF0RTZSNiHQ45_YXmJ3ovcl5k1Pcw89nbgYZYZtgvwBYlN2pakD5INaKD9tHEw)** 59 | 60 | Multi-view multi-objective clustering-based framework for scientific document summarization using citation context, **[Saini et al.](https://link.springer.com/article/10.1007/s10489-022-04166-z)** 61 | 62 | Multimodal Clustering for Community Detection, **[Ignatov et al.](https://link.springer.com/chapter/10.1007/978-3-319-64167-6_4)** 63 | 64 | Graph-Based Multimodal Clustering for Social Event Detection in Large Collections of Images, **[Petkos et al.](https://link.springer.com/chapter/10.1007/978-3-319-04114-8_13)** 65 | 66 | Social event detection using multimodal clustering and integrating supervisory signals, **[Petkos et al.](https://dl.acm.org/doi/abs/10.1145/2324796.2324825)** 67 | 68 | ## Industrial multi-view/modal 69 | 70 | Multi-View Clustering-Based Time Series Empirical Tropospheric Delay Correction, **[Gao et al.](https://ieeexplore.ieee.org/abstract/document/10121341)** 71 | 72 | An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering, **[Han et al.](https://www.mdpi.com/1996-1073/15/16/6054)** 73 | 74 | An Analysis of Current Trends in CBR Research Using Multiview Clustering, **[Greene et al.](https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2243)** 75 | 76 | Multigraph Spectral Clustering for Joint Content Delivery and Scheduling in Beam-Free Satellite Communications, **[Vázquez et al.](https://ieeexplore.ieee.org/abstract/document/9053805)** 77 | 78 | ## Other machine learning tasks 79 | 80 | Deep Multimodal Clustering for Unsupervised Audiovisual Learning, **[Hu et al.](https://openaccess.thecvf.com/content_CVPR_2019/html/Hu_Deep_Multimodal_Clustering_for_Unsupervised_Audiovisual_Learning_CVPR_2019_paper.html)** 81 | 82 | Multi-view clustering based on graph-regularized nonnegative matrix factorization for object recognition, **[Zhang et al.](https://www.sciencedirect.com/science/article/pii/S0020025517311015?casa_token=tEmaDKHmUV0AAAAA:TJOLFTZsIM0vBWX_SsfNY1KrGzQIPgxH5q1w7uDmXHdVfnSGAq7e_fDfYWaz02zjl1AUIIE_hw)** 83 | 84 | MMatch: Semi-Supervised Discriminative Representation Learning for Multi-View Classification, **[Wang et al.](https://ieeexplore.ieee.org/abstract/document/9733884)** 85 | 86 | Attributed multiplex graph clustering: A heuristic clustering-aware network embedding approach, **[Han et al.](https://www.sciencedirect.com/science/article/pii/S0378437121009699?casa_token=5eAu_aXSc9wAAAAA:QaC4ulblpVc1PT3jnq1BrvLwEc3sQ_GXuyVpBRxzIIEcPxyzLjuuIMf-cQUDouulSO1PFhUlFg)** 87 | 88 | # 89 | 90 | To be updated... 91 | --------------------------------------------------------------------------------