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Clustering is a fundamental machine learning task which has been widely studied in the literature.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Hierarchical topic mining via joint spherical tree and text embedding. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1908–1917
Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Chao Zhang, and Jiawei Han. 2020 · 1917
Earlier work this paper cites.
Deep subspace clustering with sparsity prior.. In IJCAI . 1925–1931
Xi Peng, Shijie Xiao, Jiashi Feng, Wei-Yun Yau, and Zhang Yi. 2016 · 1931
Earlier work this paper cites.
Image segmentation: A survey of graph-cut methods. In 2012 international conference on systems and informatics (ICSAI2012) . IEEE, 1936–1941
Faliu Yi and Inkyu Moon. 2012 · 1941
Earlier work this paper cites.
The Hungarian method for the assignment problem
Harold W Kuhn. 1955 · 1955
Earlier work this paper cites.
Weighted graph cuts without eigenvectors a multilevel approach
Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis. 2007 · 1957
Earlier work this paper cites.
Hierarchical clustering schemes
Stephen C Johnson. 1967 · 1967
Earlier work this paper cites.
Image segmentation by clustering
Guy Barrett Coleman and Harry C Andrews. 1979 · 1979
Earlier work this paper cites.
Least squares quantization in PCM
Stuart Lloyd. 1982 · 1982
Earlier work this paper cites.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1985 · 1985
Earlier work this paper cites.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch. 1991 · 1991
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise.. In kdd , Vol. 96. 226–231
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
BIRCH: an efficient data clustering method for very large databases
Tian Zhang, Raghu Ramakrishnan, and Miron Livny. 1996 · 1996
Earlier work this paper cites.
CURE: An efficient clustering algorithm for large databases
Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. 1998 · 1998
Earlier work this paper cites.
A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Density-based clustering in spatial databases: The algorithm gdbscan and its applications
Jörg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. 1998 · 1998
Earlier work this paper cites.
A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings 14th International Conference on Data Engineering . IEEE, 324–331
Xiaowei Xu, Martin Ester, H-P Kriegel, and Jörg Sander. 1998 · 1998
Earlier work this paper cites.
Data clustering: a review
Anil K Jain, M Narasimha Murty, and Patrick J Flynn. 1999 · 1999
Earlier work this paper cites.
The infinite Gaussian mixture model
Carl Rasmussen. 1999 · 1999
Earlier work this paper cites.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik. 2000 · 2000
Earlier work this paper cites.
A comparison of document clustering techniques
Michael Steinbach, George Karypis, and Vipin Kumar. 2000 · 2000
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Andrew Ng, Michael Jordan, and Yair Weiss. 2001 · 2001
Earlier work this paper cites.
Mean shift: A robust approach toward feature space analysis
Dorin Comaniciu and Peter Meer. 2002 · 2002
Earlier work this paper cites.
Self-organization and identification of web communities
Gary William Flake, Steve Lawrence, C Lee Giles, and Frans M Coetzee. 2002 · 2002
Earlier work this paper cites.
Community structure in social and biological networks
Michelle Girvan and Mark EJ Newman. 2002 · 2002
Earlier work this paper cites.
Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004 · 2004
Earlier work this paper cites.
Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. 2005 · 2005
Earlier work this paper cites.
Using topic keyword clusters for automatic document clustering
Hsi-Cheng Chang and Chiun-Chieh Hsu. 2005 · 2005
Earlier work this paper cites.
Survey of clustering algorithms
Rui Xu and Donald Wunsch. 2005 · 2005
Earlier work this paper cites.
Image segmentation by histogram thresholding using hierarchical cluster analysis
Agus Zainal Arifin and Akira Asano. 2006 · 2006
Earlier work this paper cites.
A survey of clustering data mining techniques
Pavel Berkhin. 2006 · 2006
Earlier work this paper cites.
Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining . SIAM, 328–339
Feng Cao, Martin Ester, Weining Qian, and Aoying Zhou. 2006 · 2006
Earlier work this paper cites.
Discovering significant opsm subspace clusters in massive gene expression data. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining . 922–928
Byron J Gao, Obi L Griffith, Martin Ester, and Steven JM Jones. 2006 · 2006
Earlier work this paper cites.
Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining . 510–519
Flavia Moser, Rong Ge, and Martin Ester. 2007 · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg. 2007 · 2007
Earlier work this paper cites.
Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008 · 2008
Earlier work this paper cites.
The group lasso for logistic regression
Lukas Meier, Sara Van De Geer, and Peter Bühlmann. 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Monte carlo methods
Malvin H Kalos and Paula A Whitlock. 2009 · 2009
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2009 · 2009
Earlier work this paper cites.
A simple and fast algorithm for K-medoids clustering
Hae-Sang Park and Chi-Hyuck Jun. 2009 · 2009
Earlier work this paper cites.
Gaussian mixture models
Douglas A Reynolds. 2009 · 2009
Earlier work this paper cites.
Sparse subspace clustering. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 00 , Vol. 6. 2790–2797
Ehsan Elhamifar René Vidal. 2009 · 2009
Earlier work this paper cites.
Inferring cancer subnetwork markers using density-constrained biclustering
Phuong Dao, Recep Colak, Raheleh Salari, Flavia Moser, Elai Davicioni, Alexander Schönhuth, and Martin Ester. 2010 · 2010
Earlier work this paper cites.
Community detection in graphs
Santo Fortunato. 2010 · 2010
Earlier work this paper cites.
Overview on techniques in cluster analysis
Itziar Frades and Rune Matthiesen. 2010 · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Generic title labeling for clustered documents
Yuen-Hsien Tseng. 2010 · 2010
Earlier work this paper cites.
Anomaly detection in temperature data using DBSCAN algorithm. In 2011 international symposium on innovations in intelligent systems and applications . IEEE, 91–95
Mete Çelik, Filiz Dadaşer-Çelik, and Ahmet Şakir Dokuz. 2011 · 2011
Earlier work this paper cites.
Anomaly detection based on enhanced DBScan algorithm
Zhenguo Chen and Yong Fei Li. 2011 · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Scalable clustering of signed networks using balance normalized cut. In Proceedings of the 21st ACM international conference on Information and knowledge management . 615–624
Kai-Yang Chiang, Joyce Jiyoung Whang, and Inderjit S Dhillon. 2012 · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
A comparative study of efficient initialization methods for the k-means clustering algorithm
M Emre Celebi, Hassan A Kingravi, and Patricio A Vela. 2013 · 2013
Earlier work this paper cites.
Probabilistic latent semantic analysis
Thomas Hofmann. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML , Vol. 3. 896
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Auto-encoder based data clustering. In Iberoamerican congress on pattern recognition . Springer, 117–124
Chunfeng Song, Feng Liu, Yongzhen Huang, Liang Wang, and Tieniu Tan. 2013 · 2013
Earlier work this paper cites.
Overlapping community detection in networks: The state-of-the-art and comparative study
Jierui Xie, Stephen Kelley, and Boleslaw K Szymanski. 2013 · 2013
Earlier work this paper cites.
A review on multi-label learning algorithms
Min-Ling Zhang and Zhi-Hua Zhou. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Deep embedding network for clustering. In 2014 22nd International conference on pattern recognition . IEEE, 1532–1537
Peihao Huang, Yan Huang, Wei Wang, and Liang Wang. 2014 · 2014
Earlier work this paper cites.
Equitability, mutual information, and the maximal information coefficient
Justin B Kinney and Gurinder S Atwal. 2014 · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents. In International conference on machine learning . PMLR, 1188–1196
Quoc Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Learning deep representations for graph clustering. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 28
Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu. 2014 · 2014
Earlier work this paper cites.
Bag constrained structure pattern mining for multi-graph classification
Jia Wu, Xingquan Zhu, Chengqi Zhang, and S Yu Philip. 2014 · 2014
Earlier work this paper cites.
Tripartite graph clustering for dynamic sentiment analysis on social media. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data . 1531–1542
Linhong Zhu, Aram Galstyan, James Cheng, and Kristina Lerman. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks. In International conference on machine learning . PMLR, 97–105
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015 · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms. In International conference on machine learning . PMLR, 843–852
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov. 2015 · 2015
Earlier work this paper cites.
A comprehensive survey of clustering algorithms
Dongkuan Xu and Yingjie Tian. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Variational deep embedding: An unsupervised and generative approach to clustering
Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016 · 2016
Earlier work this paper cites.
Parallel local graph clustering
Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, and Michael W Mahoney. 2016 · 2016
Earlier work this paper cites.
Training deep neural networks on imbalanced data sets. In 2016 international joint conference on neural networks (IJCNN) . IEEE, 4368–4374
Shoujin Wang, Wei Liu, Jia Wu, Longbing Cao, Qinxue Meng, and Paul J Kennedy. 2016b · 2016
Cited alongside, same era.
CCCF: Improving collaborative filtering via scalable user-item co-clustering. In Proceedings of the ninth ACM international conference on web search and data mining . 73–82
Yao Wu, Xudong Liu, Min Xie, Martin Ester, and Qing Yang. 2016 · 2016
Cited alongside, same era.
Unsupervised deep embedding for clustering analysis. In International conference on machine learning . PMLR, 478–487
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Multi-instance graphical transfer clustering for traffic data learning. In 2016 International Joint Conference on Neural Networks (IJCNN) . IEEE, 4390–4395
Shan Xue, Jie Lu, Jia Wu, Guangquan Zhang, and Li Xiong. 2016 · 2016
Cited alongside, same era.
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
Jian Hu, Xiangjie Li, Gang Hu, Yafei Lyu, Katalin Susztak, and Mingyao Li. 2020 · 2020
Later among the works it cites.
Deep semantic clustering by partition confidence maximisation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8849–8858
Jiabo Huang, Shaogang Gong, and Xiatian Zhu. 2020 · 2020
Later among the works it cites.
A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2020a · 2020
Later among the works it cites.
Green market segmentation and consumer profiling: a cluster approach to an emerging consumer market
Deepak Jaiswal, Vikrant Kaushal, Pankaj Kumar Singh, and Abhijeet Biswas. 2020b · 2020
Later among the works it cites.
Variational Deep Embedding Clustering by Augmented Mutual Information Maximization. In 2020 25th International Conference on Pattern Recognition (ICPR) . IEEE, 2196–2202
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Joint unsupervised learning of deep representations and image clusters. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5147–5156
Jianwei Yang, Devi Parikh, and Dhruv Batra. 2016 · 2016
Cited alongside, same era.
Unsupervised Feature Learning from Time Series.. In IJCAI . New York, USA, 2322–2328
Qin Zhang, Jia Wu, Hong Yang, Yingjie Tian, and Chengqi Zhang. 2016 · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks. In International conference on machine learning . PMLR, 214–223
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
Cited alongside, same era.
Data miners’ little helper: data transformation activity cues for cluster analysis on document collections. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics . 1–6
Tania Cerquitelli, Evelina Di Corso, Francesco Ventura, and Silvia Chiusano. 2017 · 2017
Cited alongside, same era.
Deep adaptive image clustering. In Proceedings of the IEEE international conference on computer vision . 5879–5887
Jianlong Chang, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2017 · 2017
Cited alongside, same era.
Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In Proceedings of the IEEE international conference on computer vision . 5736–5745
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, and Heng Huang. 2017 · 2017
Cited alongside, same era.
Combining structured node content and topology information for networked graph clustering
Ting Guo, Jia Wu, Xingquan Zhu, and Chengqi Zhang. 2017b · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Qiang Ji, Yanfeng Sun, Yongli Hu, and Baocai Yin. 2021b · 2020
Later among the works it cites.
Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian. 2020 · 2020
Later among the works it cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Later among the works it cites.
Learning to cluster documents into workspaces using large scale activity logs. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2416–2424
Weize Kong, Michael Bendersky, Marc Najork, Brandon Vargo, and Mike Colagrosso. 2020 · 2020
Later among the works it cites.
Contrastive representation learning: A framework and review
Phuc H Le-Khac, Graham Healy, and Alan F Smeaton. 2020 · 2020
Later among the works it cites.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. 2020d · 2020
Later among the works it cites.
Schain-iram: An efficient and effective semi-supervised clustering algorithm for attributed heterogeneous information networks
Xiang Li, Yao Wu, Martin Ester, Ben Kao, Xin Wang, and Yudian Zheng. 2020b · 2020
Later among the works it cites.
A text document clustering method based on weighted Bert model. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) , Vol. 1. IEEE, 1426–1430
Yutong Li, Juanjuan Cai, and Jingling Wang. 2020a · 2020
Later among the works it cites.
Unsupervised clustering through gaussian mixture variational autoencoder with non-reparameterized variational inference and std annealing. In 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–8
Zhihan Li, Youjian Zhao, Haowen Xu, Wenxiao Chen, Shangqing Xu, Yilin Li, and Dan Pei. 2020c · 2020
Later among the works it cites.
LR-SMOTE—An improved unbalanced data set oversampling based on K-means and SVM
XW Liang, AP Jiang, T Li, YY Xue, and GT Wang. 2020 · 2020
Later among the works it cites.
Deep learning for community detection: progress, challenges and opportunities
Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, and Philip S Yu. 2020 · 2020
Later among the works it cites.
Graph embedded pose clustering for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10539–10547
Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan. 2020 · 2020
Later among the works it cites.
Learning to cluster under domain shift. In European Conference on Computer Vision . Springer, 736–752
Willi Menapace, Stéphane Lathuilière, and Elisa Ricci. 2020 · 2020
Later among the works it cites.
Gatcluster: Self-supervised gaussian-attention network for image clustering. In European Conference on Computer Vision . Springer, 735–751
Chuang Niu, Jun Zhang, Ge Wang, and Jimin Liang. 2020 · 2020
Later among the works it cites.
Variational clustering: Leveraging variational autoencoders for image clustering. In 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–10
Vignesh Prasad, Dipanjan Das, and Brojeshwar Bhowmick. 2020 · 2020
Later among the works it cites.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
Later among the works it cites.
Unsupervised domain adaptation via structurally regularized deep clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 8725–8735
Hui Tang, Ke Chen, and Kui Jia. 2020 · 2020
Later among the works it cites.
Mice: Mixture of contrastive experts for unsupervised image clustering. In International Conference on Learning Representations
Tsung Wei Tsai, Chongxuan Li, and Jun Zhu. 2020 · 2020
Later among the works it cites.
Graph clustering with graph neural networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel Müller. 2020 · 2020
Later among the works it cites.
Scan: Learning to classify images without labels. In European Conference on Computer Vision . Springer, 268–285
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. 2020 · 2020
Later among the works it cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning . PMLR, 9929–9939
Tongzhou Wang and Phillip Isola. 2020 · 2020
Later among the works it cites.
Cluster attention contrast for video anomaly detection. In Proceedings of the 28th ACM International Conference on Multimedia . 2463–2471
Ziming Wang, Yuexian Zou, and Zeming Zhang. 2020 · 2020
Later among the works it cites.
Multi-label active learning algorithms for image classification: Overview and future promise
Jian Wu, Victor S Sheng, Jing Zhang, Hua Li, Tetiana Dadakova, Christine Leon Swisher, Zhiming Cui, and Pengpeng Zhao. 2020 · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Later among the works it cites.
Ad-cluster: Augmented discriminative clustering for domain adaptive person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9021–9030
Yunpeng Zhai, Shijian Lu, Qixiang Ye, Xuebo Shan, Jie Chen, Rongrong Ji, and Yonghong Tian. 2020 · 2020
Later among the works it cites.
Learning temporal interaction graph embedding via coupled memory networks. In Proceedings of the web conference 2020 . 3049–3055
Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhao Li, and Can Wang. 2020a · 2020
Later among the works it cites.
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020b · 2020
Later among the works it cites.
Deep robust clustering by contrastive learning
Huasong Zhong, Chong Chen, Zhongming Jin, and Xian-Sheng Hua. 2020 · 2020
Later among the works it cites.
Cross multi-type objects clustering in attributed heterogeneous information network
Sheng Zhou, Jiajun Bu, Zhen Zhang, Can Wang, Lingzhou Ma, and Jianfeng Zhang. 2020a · 2020
Later among the works it cites.
Deep learning for learning graph representations
Wenwu Zhu, Xin Wang, and Peng Cui. 2020 · 2020
Later among the works it cites.
A survey of unsupervised generative models for exploratory data analysis and representation learning
Mohanad Abukmeil, Stefano Ferrari, Angelo Genovese, Vincenzo Piuri, and Fabio Scotti. 2021 · 2021
Later among the works it cites.
Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling
Nabil Alami, Mohammed Meknassi, Noureddine En-nahnahi, Yassine El Adlouni, and Ouafae Ammor. 2021 · 2021
Later among the works it cites.
Doubly contrastive deep clustering
Zhiyuan Dang, Cheng Deng, Xu Yang, and Heng Huang. 2021a · 2021
Later among the works it cites.
Clustering by maximizing mutual information across views. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 9928–9938
Kien Do, Truyen Tran, and Svetha Venkatesh. 2021 · 2021
Later among the works it cites.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
Xiang Fang, Yuchong Hu, Pan Zhou, and Dapeng Oliver Wu. 2021 · 2021
Later among the works it cites.
A large-scale study on unsupervised spatiotemporal representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3299–3309
Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, and Kaiming He. 2021 · 2021
Later among the works it cites.
Learning the Precise Feature for Cluster Assignment
Yanhai Gan, Xinghui Dong, Huiyu Zhou, Feng Gao, and Junyu Dong. 2021 · 2021
Later among the works it cites.
A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star
Bryar A Hassan, Tarik A Rashid, and Hozan K Hamarashid. 2021 · 2021
Later among the works it cites.
Deep clustering by semantic contrastive learning
Jiabo Huang and Shaogang Gong. 2021 · 2021
Later among the works it cites.
Exploring Non-Contrastive Representation Learning for Deep Clustering
Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan. 2021 · 2021
Later among the works it cites.
A survey on generative adversarial networks: Variants, applications, and training
Abdul Jabbar, Xi Li, and Bourahla Omar. 2021 · 2021
Later among the works it cites.
A Decoder-Free Variational Deep Embedding for Unsupervised Clustering
Qiang Ji, Yanfeng Sun, Junbin Gao, Yongli Hu, and Baocai Yin. 2021a · 2021
Later among the works it cites.
A survey of community detection approaches: From statistical modeling to deep learning
Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip Yu, and Weixiong Zhang. 2021 · 2021
Later among the works it cites.
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
Turkay Kart, Wenjia Bai, Ben Glocker, and Daniel Rueckert. 2021 · 2021
Later among the works it cites.
Disentangled Contrastive Learning on Graphs
Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021b · 2021
Later among the works it cites.
Contrastive clustering. In 2021 AAAI Conference on Artificial Intelligence (AAAI)
Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng. 2021a · 2021
Later among the works it cites.
The emerging trends of multi-label learning
Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor Tsang. 2021a · 2021
Later among the works it cites.
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021b · 2021
Later among the works it cites.
Pseudo-supervised deep subspace clustering
Juncheng Lv, Zhao Kang, Xiao Lu, and Zenglin Xu. 2021 · 2021
Later among the works it cites.
A comprehensive survey on graph anomaly detection with deep learning
Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z Sheng, Hui Xiong, and Leman Akoglu. 2021 · 2021
Later among the works it cites.
A new clustering method for the diagnosis of CoVID19 using medical images
Himanshu Mittal, Avinash Chandra Pandey, Raju Pal, and Ashish Tripathi. 2021 · 2021
Later among the works it cites.
Spice: Semantic pseudo-labeling for image clustering
Chuang Niu, Hongming Shan, and Ge Wang. 2021 · 2021
Later among the works it cites.
Image clustering using an augmented generative adversarial network and information maximization
Foivos Ntelemis, Yaochu Jin, and Spencer A Thomas. 2021a · 2021
Later among the works it cites.
Information Maximization Clustering via Multi-View Self-Labelling
Foivos Ntelemis, Yaochu Jin, and Spencer A Thomas. 2021b · 2021
Later among the works it cites.
Improving unsupervised image clustering with robust learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12278–12287
Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, and Meeyoung Cha. 2021 · 2021
Later among the works it cites.
Deep video action clustering via spatio-temporal feature learning
Bo Peng, Jianjun Lei, Huazhu Fu, Yalong Jia, Zongqian Zhang, and Yi Li. 2021 · 2021
Later among the works it cites.
Spatiotemporal contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6964–6974
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui. 2021 · 2021
Later among the works it cites.
Learning Statistical Representation with Joint Deep Embedded Clustering
Mina Rezaei, Emilio Dorigatti, David Ruegamer, and Bernd Bischl. 2021 · 2021
Later among the works it cites.
You never cluster alone
Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip Torr, and Ling Shao. 2021 · 2021
Later among the works it cites.
Cluster Analysis with Deep Embeddings and Contrastive Learning
Ramakrishnan Sundareswaran, Jansel Herrera-Gerena, John Just, and Ali Jannesari. 2021 · 2021
Later among the works it cites.
Clustering-friendly representation learning via instance discrimination and feature decorrelation
Yaling Tao, Kentaro Takagi, and Kouta Nakata. 2021 · 2021
Later among the works it cites.
A hybrid approach for text document clustering using Jaya optimization algorithm
Karpagalingam Thirumoorthy and Karuppaiah Muneeswaran. 2021 · 2021
Later among the works it cites.
Multivariate weather anomaly detection using DBSCAN clustering algorithm. In Journal of Physics: Conference Series , Vol. 1869. IOP Publishing, 012077
S Wibisono, MT Anwar, A Supriyanto, and IHA Amin. 2021 · 2021
Later among the works it cites.
Supporting clustering with contrastive learning
Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew Arnold, and Bing Xiang. 2021b · 2021
Later among the works it cites.
Hongjing Zhang and Ian Davidson. 2021 · 2021
Later among the works it cites.
Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
Jiong Zhang, Wei-cheng Chang, Hsiang-fu Yu, and Inderjit Dhillon. 2021a · 2021
Later among the works it cites.
Graph contrastive clustering. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 9224–9233
Huasong Zhong, Jianlong Wu, Chong Chen, Jianqiang Huang, Minghua Deng, Liqiang Nie, Zhouchen Lin, and Xian-Sheng Hua. 2021 · 2021
Later among the works it cites.
Cluster adaptation networks for unsupervised domain adaptation
Qiang Zhou, Shirui Wang, et al · 2021
Later among the works it cites.
Self-Supervised Image Representation Learning with Geometric Set Consistency
Nenglun Chen, Lei Chu, Hao Pan, Yan Lu, and Wenping Wang. 2022 · 2022
Closest in time.
Sign prediction in sparse social networks using clustering and collaborative filtering
Mina Nasrazadani, Afsaneh Fatemi, and Mohammadali Nematbakhsh. 2022 · 2022
Closest in time.
A comprehensive survey on community detection with deep learning
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, et al · 2022
Closest in time.
Neural generative model for clustering by separating particularity and commonality
Wenqing Wang, Junpeng Bao, and Siyao Guo. 2022 · 2022
Closest in time.
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Jingya Zhou, Ling Liu, Wenqi Wei, and Jianxi Fan. 2022 · 2022
Closest in time.
A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks
Hossein Saeedi Emadi and Sayyed Majid Mazinani. 2018 · 2035
Closest in time.