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The simultaneous partitioning of all the modes of the input data tensors (rows and columns in a data matrix) is both a method for improving clustering on one mode while performing dimensionality reduction on the other mode(s), and a tool for providing an actionable interpretation of the clusters in the main mode as summaries of the features in each other mode(s). Hence, it is useful in many complex decision systems and data science applications. In this article, we survey the the co-clustering literature by reviewing the main co-clustering methods, with a special focus on the work done in the past 25 years. We identify, describe, and compare the main algorithmic categories and provide a practical characterization with respect to similar unsupervised techniques. Additionally, we try to explain why it is still a powerful tool despite the apparent recent decreasing interest shown by the machine learning community. To this purpose, we review the most recent trends in co-clustering research and outline the open problems and promising future research perspectives.<\/jats:p>","DOI":"10.1145\/3698875","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T09:32:27Z","timestamp":1728034347000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5762-1979","authenticated-orcid":false,"given":"Elena","family":"Battaglia","sequence":"first","affiliation":[{"name":"Computer Science, Universit\u00e0 degli Studi di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7648-162X","authenticated-orcid":false,"given":"Federico","family":"Peiretti","sequence":"additional","affiliation":[{"name":"Computer Science, Universit\u00e0 degli Studi di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5145-3438","authenticated-orcid":false,"given":"Ruggero Gaetano","family":"Pensa","sequence":"additional","affiliation":[{"name":"Computer Science, Universit\u00e0 degli Studi di Torino, Turin, Italy"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"100435","DOI":"10.1016\/j.cosrev.2021.100435","article-title":"Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms","volume":"42","author":"Abdolali Maryam","year":"2021","unstructured":"Maryam Abdolali and Nicolas Gillis. 2021. 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Block clustering for web pages categorization. In Proceedings of the IDEAL. 260\u2013267."},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.knosys.2013.05.006","article-title":"Preference-based clustering reviews for augmenting e-commerce recommendation","volume":"50","author":"Chen Li","year":"2013","unstructured":"Li Chen and Feng Wang. 2013. Preference-based clustering reviews for augmenting e-commerce recommendation. Knowl. Based Syst. 50 (2013), 44\u201359.","journal-title":"Knowl. Based Syst."},{"issue":"7","key":"e_1_3_3_40_2","first-page":"6930","article-title":"Fast flexible bipartite graph model for co-clustering","volume":"35","author":"Chen Wei","year":"2023","unstructured":"Wei Chen, Hongjun Wang, Zhiguo Long, and Tianrui Li. 2023. Fast flexible bipartite graph model for co-clustering. IEEE Trans. Knowl. Data Eng. 35, 7 (2023), 6930\u20136940.","journal-title":"IEEE Trans. Knowl. 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In Proceedings of the SIAM SDM. 114\u2013125."},{"issue":"1","key":"e_1_3_3_46_2","first-page":"41:1\u201341:16","article-title":"Scalable non-negative matrix tri-factorization","volume":"10","author":"Copar Andrej","year":"2017","unstructured":"Andrej Copar, Marinka Zitnik, and Blaz Zupan. 2017. Scalable non-negative matrix tri-factorization. BioData Min. 10, 1 (2017), 41:1\u201341:16.","journal-title":"BioData Min."},{"issue":"4","key":"e_1_3_3_47_2","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1080\/10618600.2020.1739533","article-title":"Co-clustering of ordinal data via latent continuous random variables and not missing at random entries","volume":"29","author":"Corneli Marco","year":"2020","unstructured":"Marco Corneli, Charles Bouveyron, and Pierre Latouche. 2020. Co-clustering of ordinal data via latent continuous random variables and not missing at random entries. J. Comput. Graph. Stat. 29, 4 (2020), 771\u2013785.","journal-title":"J. Comput. Graph. 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Surv."},{"key":"e_1_3_3_53_2","doi-asserted-by":"crossref","first-page":"107101","DOI":"10.1016\/j.knosys.2021.107101","article-title":"Tri-regularized nonnegative matrix tri-factorization for co-clustering","volume":"226","author":"Deng Ping","year":"2021","unstructured":"Ping Deng, Tianrui Li, Hongjun Wang, Shi-Jinn Horng, Zeng Yu, and Xiaomin Wang. 2021. Tri-regularized nonnegative matrix tri-factorization for co-clustering. Knowl. Based Syst. 226 (2021), 107101.","journal-title":"Knowl. Based Syst."},{"key":"e_1_3_3_54_2","first-page":"269","volume-title":"Proceedings of the ACM SIGKDD","author":"Dhillon Inderjit S.","year":"2001","unstructured":"Inderjit S. Dhillon. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the ACM SIGKDD, Doheon Lee, Mario Schkolnick, Foster J. Provost, and Ramakrishnan Srikant (Eds.). 269\u2013274."},{"key":"e_1_3_3_55_2","first-page":"89","volume-title":"Proceedings of the ACM SIGKDD","author":"Dhillon Inderjit S.","year":"2003","unstructured":"Inderjit S. Dhillon, Subramanyam Mallela, and Dharmendra S. Modha. 2003. Information-theoretic co-clustering. In Proceedings of the ACM SIGKDD. 89\u201398."},{"key":"e_1_3_3_56_2","first-page":"606","volume-title":"Proceedings of the SIAM SDM","author":"Ding Chris H. Q.","year":"2005","unstructured":"Chris H. Q. Ding and Xiaofeng He. 2005. On the equivalence of nonnegative matrix factorization and spectral clustering. In Proceedings of the SIAM SDM. 606\u2013610."},{"key":"e_1_3_3_57_2","first-page":"126","volume-title":"Proceedings of the ACM SIGKDD","author":"Ding Chris H. Q.","year":"2006","unstructured":"Chris H. Q. Ding, Tao Li, Wei Peng, and Haesun Park. 2006. Orthogonal nonnegative matrix t-factorizations for clustering. In Proceedings of the ACM SIGKDD. 126\u2013135."},{"key":"e_1_3_3_58_2","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1109\/TSP.2021.3101979","article-title":"Differentiable bi-sparse multi-view co-clustering","volume":"69","author":"Du Shide","year":"2021","unstructured":"Shide Du, Zhanghui Liu, Zhaoliang Chen, Wenyuan Yang, and Shiping Wang. 2021. Differentiable bi-sparse multi-view co-clustering. IEEE Trans. Signal Process. 69 (2021), 4623\u20134636.","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"e_1_3_3_59_2","first-page":"211","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork and Aaron Roth. 2014. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 3\u20134 (2014), 211\u2013407.","journal-title":"Found. Trends Theor. Comput. Sci."},{"issue":"1","key":"e_1_3_3_60_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10618-013-0341-y","article-title":"Link prediction in heterogeneous data via generalized coupled tensor factorization","volume":"29","author":"Ermis Beyza","year":"2015","unstructured":"Beyza Ermis, Evrim Acar, and Ali Taylan Cemgil. 2015. Link prediction in heterogeneous data via generalized coupled tensor factorization. Data Min. Knowl. Discov. 29, 1 (2015), 203\u2013236.","journal-title":"Data Min. Knowl. Discov."},{"key":"e_1_3_3_61_2","article-title":"Improving performances of top-N recommendations with co-clustering method","volume":"143","author":"Feng Liang","year":"2020","unstructured":"Liang Feng, Qianchuan Zhao, and Cangqi Zhou. 2020. Improving performances of top-N recommendations with co-clustering method. Expert Syst. Appl. 143 (2020).","journal-title":"Expert Syst. Appl."},{"key":"e_1_3_3_62_2","volume-title":"Proceedings of the NeurIPS","author":"Fettal Chakib","year":"2022","unstructured":"Chakib Fettal, Lazhar Labiod, and Mohamed Nadif. 2022. Efficient and effective optimal transport-based biclustering. In Proceedings of the NeurIPS."},{"issue":"3","key":"e_1_3_3_63_2","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/TKDE.2023.3300814","article-title":"Boosting subspace co-clustering via bilateral graph convolution","volume":"36","author":"Fettal Chakib","year":"2024","unstructured":"Chakib Fettal, Lazhar Labiod, and Mohamed Nadif. 2024. Boosting subspace co-clustering via bilateral graph convolution. IEEE Trans. Knowl. Data Eng. 36, 3 (2024), 960\u2013971.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"e_1_3_3_64_2","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patcog.2007.05.018","article-title":"A survey of kernel and spectral methods for clustering","volume":"41","author":"Filippone Maurizio","year":"2008","unstructured":"Maurizio Filippone, Francesco Camastra, Francesco Masulli, and Stefano Rovetta. 2008. A survey of kernel and spectral methods for clustering. Pattern Recognit. 41, 1 (2008), 176\u2013190.","journal-title":"Pattern Recognit."},{"issue":"2","key":"e_1_3_3_65_2","first-page":"e1349","article-title":"Surveying the reach and maturity of machine learning and artificial intelligence in astronomy","volume":"10","author":"Fluke Christopher J.","year":"2020","unstructured":"Christopher J. Fluke and Colin Jacobs. 2020. Surveying the reach and maturity of machine learning and artificial intelligence in astronomy. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 10, 2 (2020), e1349.","journal-title":"Wiley Interdisc. Rev.: Data Min. Knowl. Discov."},{"key":"e_1_3_3_66_2","first-page":"1497","volume-title":"Proceedings of the EUSIPCO","author":"Forero Pedro A.","year":"2020","unstructured":"Pedro A. Forero and Paul A. Baxley. 2020. Tucker-regularized tensor Bregman co-clustering. In Proceedings of the EUSIPCO. 1497\u20131501."},{"key":"e_1_3_3_67_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neucom.2020.02.104","article-title":"An overview of recent multi-view clustering","volume":"402","author":"Fu Lele","year":"2020","unstructured":"Lele Fu, Pengfei Lin, Athanasios V. Vasilakos, and Shiping Wang. 2020. An overview of recent multi-view clustering. Neurocomputing 402 (2020), 148\u2013161.","journal-title":"Neurocomputing"},{"key":"e_1_3_3_68_2","first-page":"41","volume-title":"Proceedings of the ACM SIGKDD","author":"Gao Bin","year":"2005","unstructured":"Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, and Wei-Ying Ma. 2005. Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering. In Proceedings of the ACM SIGKDD. 41\u201350."},{"key":"e_1_3_3_69_2","first-page":"625","volume-title":"Proceedings of the IEEE ICDM","author":"George Thomas","year":"2005","unstructured":"Thomas George and Srujana Merugu. 2005. A scalable collaborative filtering framework based on co-clustering. In Proceedings of the IEEE ICDM. 625\u2013628."},{"key":"e_1_3_3_70_2","first-page":"732","article-title":"Measures of association for cross classification","volume":"49","author":"Goodman L. A.","year":"1954","unstructured":"L. A. Goodman and W. H. Kruskal. 1954. Measures of association for cross classification. J. Am. Stat. Assoc. 49 (1954), 732\u2013764.","journal-title":"J. Am. Stat. Assoc."},{"issue":"4","key":"e_1_3_3_71_2","first-page":"437","article-title":"Simultaneous clustering of rows and columns","volume":"24","author":"Govaert G\u00e9rard","year":"1995","unstructured":"G\u00e9rard Govaert. 1995. Simultaneous clustering of rows and columns. Contr. Cybern. 24, 4 (1995), 437\u2013458.","journal-title":"Contr. Cybern."},{"issue":"2","key":"e_1_3_3_72_2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0031-3203(02)00074-2","article-title":"Clustering with block mixture models","volume":"36","author":"Govaert G\u00e9rard","year":"2003","unstructured":"G\u00e9rard Govaert and Mohamed Nadif. 2003. Clustering with block mixture models. Pattern Recognit. 36, 2 (2003), 463\u2013473.","journal-title":"Pattern Recognit."},{"issue":"6","key":"e_1_3_3_73_2","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1016\/j.csda.2007.09.007","article-title":"Block clustering with Bernoulli mixture models: Comparison of different approaches","volume":"52","author":"Govaert G\u00e9rard","year":"2008","unstructured":"G\u00e9rard Govaert and Mohamed Nadif. 2008. Block clustering with Bernoulli mixture models: Comparison of different approaches. Comput. Stat. Data Anal. 52, 6 (2008), 3233\u20133245.","journal-title":"Comput. Stat. Data Anal."},{"issue":"3","key":"e_1_3_3_74_2","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1080\/03610920903140197","article-title":"Latent block model for contingency table","volume":"39","author":"Govaert G\u00e9rard","year":"2010","unstructured":"G\u00e9rard Govaert and Mohamed Nadif. 2010. Latent block model for contingency table. Commun. Stat. - Theor. Meth. 39, 3 (2010), 416\u2013425.","journal-title":"Commun. Stat. - Theor. Meth."},{"key":"e_1_3_3_75_2","doi-asserted-by":"crossref","DOI":"10.1002\/9781118649480","volume-title":"Co-clustering: Models, Algorithms and Applications","author":"Govaert G\u00e9rard","year":"2013","unstructured":"G\u00e9rard Govaert and Mohamed Nadif. 2013. Co-clustering: Models, Algorithms and Applications. John Wiley & Sons."},{"issue":"3","key":"e_1_3_3_76_2","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s11634-016-0274-6","article-title":"Mutual information, phi-squared and model-based co-clustering for contingency tables","volume":"12","author":"Govaert G\u00e9rard","year":"2018","unstructured":"G\u00e9rard Govaert and Mohamed Nadif. 2018. Mutual information, phi-squared and model-based co-clustering for contingency tables. Adv. Data Anal. Classif. 12, 3 (2018), 455\u2013488.","journal-title":"Adv. Data Anal. Classif."},{"key":"e_1_3_3_77_2","first-page":"359","volume-title":"Proceedings of the KDD","author":"Gu Quanquan","year":"2009","unstructured":"Quanquan Gu and Jie Zhou. 2009. Co-clustering on manifolds. In Proceedings of the KDD. 359\u2013368."},{"key":"e_1_3_3_78_2","first-page":"1","article-title":"Foundation of the PARAFAC procedure: Models and conditions for an \u201cexplanatory\u201d multimodal factor analysis","volume":"16","author":"Harshman Richard A.","year":"1970","unstructured":"Richard A. Harshman. 1970. Foundation of the PARAFAC procedure: Models and conditions for an \u201cexplanatory\u201d multimodal factor analysis. UCLA Work. Pap. Phonet. 16 (1970), 1\u201384.","journal-title":"UCLA Work. Pap. Phonet."},{"issue":"337","key":"e_1_3_3_79_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01621459.1972.10481214","article-title":"Direct clustering of a data matrix","volume":"67","author":"Hartigan John A.","year":"1972","unstructured":"John A. Hartigan. 1972. Direct clustering of a data matrix. J. Am. Stat. Assoc. 67, 337 (1972), 123\u2013129.","journal-title":"J. Am. Stat. Assoc."},{"key":"e_1_3_3_80_2","first-page":"741","volume-title":"Proceedings of the ECML PKDD","author":"He Jing","year":"2018","unstructured":"Jing He, Xin Li, Lejian Liao, and Mingzhong Wang. 2018. Inferring continuous latent preference on transition intervals for next point-of-interest recommendation. In Proceedings of the ECML PKDD. 741\u2013756."},{"issue":"5","key":"e_1_3_3_81_2","first-page":"95:1\u201395:43","article-title":"Triclustering algorithms for three-dimensional data analysis: A comprehensive survey","volume":"51","author":"Henriques Rui","year":"2019","unstructured":"Rui Henriques and Sara C. Madeira. 2019. Triclustering algorithms for three-dimensional data analysis: A comprehensive survey. ACM Comput. Surv. 51, 5 (2019), 95:1\u201395:43.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_82_2","first-page":"232","volume-title":"Proceedings of the MDAI","author":"Honda Katsuhiro","year":"2018","unstructured":"Katsuhiro Honda, Shotaro Matsuzaki, Seiki Ubukata, and Akira Notsu. 2018. Privacy preserving collaborative fuzzy co-clustering of three-mode cooccurrence data. In Proceedings of the MDAI. 232\u2013242."},{"key":"e_1_3_3_83_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ins.2018.03.019","article-title":"Multi-sided recommendation based on social tensor factorization","volume":"447","author":"Hong Min-Sung","year":"2018","unstructured":"Min-Sung Hong and Jason J. Jung. 2018. Multi-sided recommendation based on social tensor factorization. Inf. Sci. 447 (2018), 140\u2013156.","journal-title":"Inf. Sci."},{"issue":"2","key":"e_1_3_3_84_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TSMCC.2008.2007252","article-title":"A survey of evolutionary algorithms for clustering","volume":"39","author":"Hruschka Eduardo R.","year":"2009","unstructured":"Eduardo R. Hruschka, Ricardo Jos\u00e9 Gabrielli Barreto Campello, Alex Alves Freitas, and Andr\u00e9 Carlos Ponce de Leon Ferreira de Carvalho. 2009. A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C 39, 2 (2009), 133\u2013155.","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"e_1_3_3_85_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.knosys.2015.03.027","article-title":"Spectral co-clustering ensemble","volume":"84","author":"Huang Shudong","year":"2015","unstructured":"Shudong Huang, Hongjun Wang, Dingcheng Li, Yan Yang, and Tianrui Li. 2015. Spectral co-clustering ensemble. Knowl. Based Syst. 84 (2015), 46\u201355.","journal-title":"Knowl. Based Syst."},{"key":"e_1_3_3_86_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2019.09.079","article-title":"Auto-weighted multi-view co-clustering with bipartite graphs","volume":"512","author":"Huang Shudong","year":"2020","unstructured":"Shudong Huang, Zenglin Xu, Ivor W. Tsang, and Zhao Kang. 2020. Auto-weighted multi-view co-clustering with bipartite graphs. Inf. Sci. 512 (2020), 18\u201330.","journal-title":"Inf. Sci."},{"issue":"1","key":"e_1_3_3_87_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert Lawrenc","year":"1985","unstructured":"Lawrenc Hubert and Phipps Arabie. 1985. Comparing partitions. J. Classif. 2, 1 (1985), 193\u2013218.","journal-title":"J. Classif."},{"issue":"1","key":"e_1_3_3_88_2","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1007\/s10489-021-02405-3","article-title":"Weighted multi-view co-clustering (WMVCC) for sparse data","volume":"52","author":"Hussain Syed Fawad","year":"2022","unstructured":"Syed Fawad Hussain, Khadija Khan, and Rashad M. Jillani. 2022. Weighted multi-view co-clustering (WMVCC) for sparse data. Appl. Intell. 52, 1 (2022), 398\u2013416.","journal-title":"Appl. Intell."},{"issue":"2","key":"e_1_3_3_89_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10618-012-0248-z","article-title":"Parameter-less co-clustering for star-structured heterogeneous data","volume":"26","author":"Ienco Dino","year":"2013","unstructured":"Dino Ienco, C\u00e9line Robardet, Ruggero G. Pensa, and Rosa Meo. 2013. Parameter-less co-clustering for star-structured heterogeneous data. Data Min. Knowl. Discov. 26, 2 (2013), 217\u2013254.","journal-title":"Data Min. Knowl. Discov."},{"key":"e_1_3_3_90_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","article-title":"K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data","volume":"622","author":"Ikotun Abiodun M.","year":"2023","unstructured":"Abiodun M. Ikotun, Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, and Heming Jia. 2023. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. 622 (2023), 178\u2013210.","journal-title":"Inf. Sci."},{"key":"e_1_3_3_91_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.csda.2018.01.014","article-title":"Model-based co-clustering for ordinal data","volume":"123","author":"Jacques Julien","year":"2018","unstructured":"Julien Jacques and Christophe Biernacki. 2018. Model-based co-clustering for ordinal data. Comput. Stat. Data Anal. 123 (2018), 101\u2013115.","journal-title":"Comput. Stat. Data Anal."},{"key":"e_1_3_3_92_2","volume-title":"Algorithms for Clustering Data","author":"Jain Anil K.","year":"1988","unstructured":"Anil K. Jain and Richard C. Dubes. 1988. Algorithms for Clustering Data. Prentice-Hall."},{"issue":"3","key":"e_1_3_3_93_2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: A review","volume":"31","author":"Jain Anil K.","year":"1999","unstructured":"Anil K. Jain, M. Narasimha Murty, and Patrick J. Flynn. 1999. Data clustering: A review. ACM Comput. Surv. 31, 3 (1999), 264\u2013323.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_94_2","first-page":"397","volume-title":"Proceedings of the ESORICS","author":"Jha Somesh","year":"2005","unstructured":"Somesh Jha, Luis Kruger, and Patrick McDaniel. 2005. Privacy preserving clustering. In Proceedings of the ESORICS. Springer, 397\u2013417."},{"issue":"1","key":"e_1_3_3_95_2","first-page":"1","article-title":"Advances and open problems in federated learning","volume":"14","year":"2021","unstructured":"Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adri\u00e0 Gasc\u00f3n, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Za\u00efd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecn\u00fd, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancr\u00e8de Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer \u00d6zg\u00fcr, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tram\u00e8r, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. 2021. Advances and open problems in federated learning. Found. Trends Mach. Learn. 14, 1\u20132 (2021), 1\u2013210.","journal-title":"Found. Trends Mach. Learn."},{"issue":"6","key":"e_1_3_3_96_2","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1007\/s11222-014-9472-2","article-title":"Estimation and selection for the latent block model on categorical data","volume":"25","author":"Keribin Christine","year":"2015","unstructured":"Christine Keribin, Vincent Brault, Gilles Celeux, and G\u00e9rard Govaert. 2015. Estimation and selection for the latent block model on categorical data. Stat. Comput. 25, 6 (2015), 1201\u20131216.","journal-title":"Stat. Comput."},{"issue":"1","key":"e_1_3_3_97_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TPAMI.2018.2876253","article-title":"Motion segmentation & multiple object tracking by correlation co-clustering","volume":"42","author":"Keuper Margret","year":"2020","unstructured":"Margret Keuper, Siyu Tang, Bjoern Andres, Thomas Brox, and Bernt Schiele. 2020. Motion segmentation & multiple object tracking by correlation co-clustering. IEEE Trans. Pattern Anal. Mach. Intell. 42, 1 (2020), 140\u2013153.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_3_98_2","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1101\/gr.648603","article-title":"Spectral biclustering of microarray cancer data: Co-clustering genes and conditions","volume":"13","author":"Kluger Yuval","year":"2003","unstructured":"Yuval Kluger, Ronen Basri, Joseph T. Chang, and Mark Gerstein. 2003. Spectral biclustering of microarray cancer data: Co-clustering genes and conditions. Genome Rese. 13 (2003), 703\u2013716.","journal-title":"Genome Rese."},{"issue":"3","key":"e_1_3_3_99_2","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda Tamara G.","year":"2009","unstructured":"Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Rev. 51, 3 (2009), 455\u2013500.","journal-title":"SIAM Rev."},{"key":"e_1_3_3_100_2","first-page":"2342","volume-title":"Proceedings of the ACM SIGSAC CCS","author":"Kolluri Aashish","year":"2021","unstructured":"Aashish Kolluri, Teodora Baluta, and Prateek Saxena. 2021. Private hierarchical clustering in federated networks. In Proceedings of the ACM SIGSAC CCS, Yongdae Kim, Jong Kim, Giovanni Vigna, and Elaine Shi (Eds.). 2342\u20132360."},{"issue":"1","key":"e_1_3_3_101_2","first-page":"1:1\u20131:58","article-title":"Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering","volume":"3","author":"Kriegel Hans-Peter","year":"2009","unstructured":"Hans-Peter Kriegel, Peer Kr\u00f6ger, and Arthur Zimek. 2009. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans. Knowl. Discov. Data 3, 1 (2009), 1:1\u20131:58.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"4","key":"e_1_3_3_102_2","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1002\/widm.1057","article-title":"Subspace clustering","volume":"2","author":"Kriegel Hans-Peter","year":"2012","unstructured":"Hans-Peter Kriegel, Peer Kr\u00f6ger, and Arthur Zimek. 2012. Subspace clustering. WIREs Data Mining Knowl. Discov. 2, 4 (2012), 351\u2013364.","journal-title":"WIREs Data Mining Knowl. Discov."},{"key":"e_1_3_3_103_2","first-page":"1140","volume-title":"Proceedings of the IEEE ICDM","author":"Labiod Lazhar","year":"2011","unstructured":"Lazhar Labiod and Mohamed Nadif. 2011. Co-clustering for binary and categorical data with maximum modularity. In Proceedings of the IEEE ICDM. 1140\u20131145."},{"issue":"5","key":"e_1_3_3_104_2","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s11222-016-9677-7","article-title":"Diagonal latent block model for binary data","volume":"27","author":"Laclau Charlotte","year":"2017","unstructured":"Charlotte Laclau and Mohamed Nadif. 2017. Diagonal latent block model for binary data. Stat. Comput. 27, 5 (2017), 1145\u20131163.","journal-title":"Stat. Comput."},{"key":"e_1_3_3_105_2","first-page":"1955","volume-title":"Proceedings of the ICML","volume":"70","author":"Laclau Charlotte","year":"2017","unstructured":"Charlotte Laclau, Ievgen Redko, Basarab Matei, Youn\u00e8s Bennani, and Vincent Brault. 2017. Co-clustering through optimal transport. In Proceedings of the ICML, Vol. 70. 1955\u20131964."},{"issue":"1","key":"e_1_3_3_106_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/bib\/bbk007","article-title":"Machine learning in bioinformatics","volume":"7","year":"2006","unstructured":"Pedro Larra\u00f1aga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, I\u00f1aki Inza, Jos\u00e9 Antonio Lozano, Rub\u00e9n Arma\u00f1anzas, Guzm\u00e1n Santaf\u00e9, Aritz P\u00e9rez Mart\u00ednez, and Victor Robles. 2006. Machine learning in bioinformatics. Brief. Bioinform. 7, 1 (2006), 86\u2013112.","journal-title":"Brief. Bioinform."},{"issue":"6","key":"e_1_3_3_107_2","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1109\/TSMCB.2012.2234108","article-title":"Relational multimanifold coclustering","volume":"43","author":"Li Ping","year":"2013","unstructured":"Ping Li, Jiajun Bu, Chun Chen, Zhanying He, and Deng Cai. 2013. Relational multimanifold coclustering. IEEE Trans. Cybern. 43, 6 (2013), 1871\u20131881.","journal-title":"IEEE Trans. Cybern"},{"key":"e_1_3_3_108_2","doi-asserted-by":"crossref","first-page":"104989","DOI":"10.1016\/j.engappai.2022.104989","article-title":"Semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization","volume":"114","author":"Li Xiangli","year":"2022","unstructured":"Xiangli Li, Xiyan Lu, and Xuezhen Fan. 2022. Semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization. Eng. Appl. Artif. Intell. 114 (2022), 104989.","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"e_1_3_3_109_2","doi-asserted-by":"crossref","first-page":"900","DOI":"10.14778\/3503585.3503598","article-title":"Federated matrix factorization with privacy guarantee","volume":"15","author":"Li Zitao","year":"2021","unstructured":"Zitao Li, Bolin Ding, Ce Zhang, Ninghui Li, and Jingren Zhou. 2021. Federated matrix factorization with privacy guarantee. Proc. VLDB Endow. 15, 4 (2021), 900\u2013913.","journal-title":"Proc. VLDB Endow."},{"issue":"6","key":"e_1_3_3_110_2","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.14778\/3583140.3583146","article-title":"Differentially private vertical federated clustering","volume":"16","author":"Li Zitao","year":"2023","unstructured":"Zitao Li, Tianhao Wang, and Ninghui Li. 2023. Differentially private vertical federated clustering. Proc. VLDB Endow. 16, 6 (2023), 1277\u20131290.","journal-title":"Proc. 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Pattern Recognit. 38, 11 (2005), 1857\u20131874.","journal-title":"Pattern Recognit."},{"key":"e_1_3_3_113_2","doi-asserted-by":"crossref","first-page":"33481","DOI":"10.1109\/ACCESS.2019.2904314","article-title":"An overview of co-clustering via matrix factorization","volume":"7","author":"Lin Renjie","year":"2019","unstructured":"Renjie Lin, Shiping Wang, and Wenzhong Guo. 2019. An overview of co-clustering via matrix factorization. IEEE Access 7 (2019), 33481\u201333493.","journal-title":"IEEE Access"},{"issue":"2","key":"e_1_3_3_114_2","first-page":"31:1\u201331:36","article-title":"When machine learning meets privacy: A survey and outlook","volume":"54","author":"Liu Bo","year":"2022","unstructured":"Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. 2022. When machine learning meets privacy: A survey and outlook. ACM Comput. Surv. 54, 2 (2022), 31:1\u201331:36.","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"e_1_3_3_115_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s13042-012-0077-9","article-title":"Spectral co-clustering documents and words using fuzzy K-harmonic means","volume":"4","author":"Liu Na","year":"2013","unstructured":"Na Liu, Fei Chen, and Mingyu Lu. 2013. Spectral co-clustering documents and words using fuzzy K-harmonic means. Int. J. Mach. Learn. Cybern. 4, 1 (2013), 75\u201383.","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"e_1_3_3_116_2","first-page":"635","volume-title":"Proceedings of the ACM SIGKDD","author":"Long Bo","year":"2005","unstructured":"Bo Long, Zhongfei (Mark) Zhang, and Philip S. Yu. 2005. Co-clustering by block value decomposition. In Proceedings of the ACM SIGKDD. 635\u2013640."},{"issue":"1","key":"e_1_3_3_117_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/TCBB.2004.2","article-title":"Biclustering algorithms for biological data analysis: A survey","volume":"1","author":"Madeira Sara C.","year":"2004","unstructured":"Sara C. Madeira and Arlindo L. Oliveira. 2004. Biclustering algorithms for biological data analysis: A survey. IEEE ACM Trans. Comput. Biol. Bioinform. 1, 1 (2004), 24\u201345.","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"key":"e_1_3_3_118_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/978-3-642-59471-7_6","volume-title":"Advances in Classification and Data Analysis","author":"Maurizio Vichi","year":"2001","unstructured":"Vichi Maurizio. 2001. Double k-means clustering for simultaneous classification of objects and variables. In Advances in Classification and Data Analysis. Springer Berlin, 43\u201352."},{"key":"e_1_3_3_119_2","article-title":"Two-mode clustering methods: A structured overview","volume":"13","author":"Mechelen Iven","year":"2004","unstructured":"Iven Mechelen, Hans-Hermann Bock, and Paul De Boeck. 2004. Two-mode clustering methods: A structured overview. Stat. Meth. Med. Res. 13 (11 2004), 363\u201394.","journal-title":"Stat. Meth. Med. Res."},{"key":"e_1_3_3_120_2","first-page":"175","volume-title":"Proceedings of the IEEE ICMLA","author":"Nadif Mohamed","year":"2010","unstructured":"Mohamed Nadif and G\u00e9rard Govaert. 2010. Model-based co-clustering for continuous data. In Proceedings of the IEEE ICMLA. 175\u2013180."},{"issue":"2","key":"e_1_3_3_121_2","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman Mark E. J.","year":"2004","unstructured":"Mark E. J. Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Phys. Rev. E 69, 2 (2004), 026113.","journal-title":"Phys. Rev. E"},{"key":"e_1_3_3_122_2","first-page":"570","volume-title":"Proceedings of the SIAM SDM","author":"Nguyen Kim-Ngan","year":"2011","unstructured":"Kim-Ngan Nguyen, Lo\u00efc Cerf, Marc Plantevit, and Jean-Fran\u00e7ois Boulicaut. 2011. Multidimensional association rules in Boolean tensors. In Proceedings of the SIAM SDM. 570\u2013581."},{"key":"e_1_3_3_123_2","doi-asserted-by":"crossref","first-page":"107207","DOI":"10.1016\/j.patcog.2020.107207","article-title":"Auto-weighted multi-view co-clustering via fast matrix factorization","volume":"102","author":"Nie Feiping","year":"2020","unstructured":"Feiping Nie, Shaojun Shi, and Xuelong Li. 2020. Auto-weighted multi-view co-clustering via fast matrix factorization. Pattern Recognit. 102 (2020), 107207.","journal-title":"Pattern Recognit."},{"key":"e_1_3_3_124_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/1250790.1250803","volume-title":"Proceedings of the STOC","author":"Nissim Kobbi","year":"2007","unstructured":"Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2007. Smooth sensitivity and sampling in private data analysis. In Proceedings of the STOC. 75\u201384."},{"key":"e_1_3_3_125_2","doi-asserted-by":"crossref","first-page":"108612","DOI":"10.1016\/j.patcog.2022.108612","article-title":"Impact of metrics on biclustering solution and quality: A review","volume":"127","author":"Noronha Marta D. M.","year":"2022","unstructured":"Marta D. M. Noronha, Rui Henriques, Sara C. Madeira, and Luis E. Z\u00e1rate. 2022. Impact of metrics on biclustering solution and quality: A review. Pattern Recognit. 127 (2022), 108612.","journal-title":"Pattern Recognit."},{"issue":"1","key":"e_1_3_3_126_2","first-page":"37","article-title":"Privacy preserving clustering by data transformation","volume":"1","author":"Oliveira Stanley R. M.","year":"2010","unstructured":"Stanley R. M. Oliveira and Osmar R. Zaiane. 2010. Privacy preserving clustering by data transformation. J. Inf. Data Manag. 1, 1 (2010), 37\u201337.","journal-title":"J. Inf. Data Manag."},{"issue":"2","key":"e_1_3_3_127_2","first-page":"16:1\u201316:68","article-title":"Systematic review of clustering high-dimensional and large datasets","volume":"12","author":"Pandove Divya","year":"2018","unstructured":"Divya Pandove, Shivani Goel, and Rinkle Rani. 2018. Systematic review of clustering high-dimensional and large datasets. ACM Trans. Knowl. Discov. Data 12, 2 (2018), 16:1\u201316:68.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_3_128_2","first-page":"512","volume-title":"Proceedings of the IEEE ICDM","author":"Papadimitriou Spiros","year":"2008","unstructured":"Spiros Papadimitriou and Jimeng Sun. 2008. DisCo: Distributed co-clustering with Map-Reduce: A case study towards petabyte-scale end-to-end mining. In Proceedings of the IEEE ICDM. 512\u2013521."},{"key":"e_1_3_3_129_2","volume-title":"Encyclopedia of Social Network Analysis and Mining, 2nd Edition","author":"Papalexakis Evangelos E.","year":"2018","unstructured":"Evangelos E. Papalexakis, Alex Beutel, and Peter Steenkiste. 2018. Network anomaly detection using co-clustering. In Encyclopedia of Social Network Analysis and Mining, 2nd Edition. Springer."},{"key":"e_1_3_3_130_2","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1145\/2740908.2743004","volume-title":"Proceedings of the WWW","author":"Papalexakis Evangelos E.","year":"2015","unstructured":"Evangelos E. Papalexakis and A. Seza Do\u011fru\u00f6z. 2015. Understanding multilingual social networks in online immigrant communities. In Proceedings of the WWW. 865\u2013870."},{"issue":"2","key":"e_1_3_3_131_2","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TSP.2012.2225052","article-title":"From k-means to higher-way co-clustering: Multilinear decomposition with sparse latent factors","volume":"61","author":"Papalexakis Evangelos E.","year":"2013","unstructured":"Evangelos E. Papalexakis, Nicholas D. Sidiropoulos, and Rasmus Bro. 2013. From k-means to higher-way co-clustering: Multilinear decomposition with sparse latent factors. IEEE Trans. Signal Process. 61, 2 (2013), 493\u2013506.","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"e_1_3_3_132_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1145\/1007730.1007731","article-title":"Subspace clustering for high dimensional data: A review","volume":"6","author":"Parsons Lance","year":"2004","unstructured":"Lance Parsons, Ehtesham Haque, and Huan Liu. 2004. Subspace clustering for high dimensional data: A review. SIGKDD Explor. 6, 1 (2004), 90\u2013105.","journal-title":"SIGKDD Explor."},{"key":"e_1_3_3_133_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/14786440009463897","article-title":"On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling","volume":"5","author":"Pearson Karl","year":"1900","unstructured":"Karl Pearson. 1900. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. 5 (1900), 157\u2013175.","journal-title":"Philos. Mag."},{"issue":"8","key":"e_1_3_3_134_2","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1109\/TFUZZ.2021.3105193","article-title":"Federated FCM: Clustering under privacy requirements","volume":"30","author":"Pedrycz Witold","year":"2022","unstructured":"Witold Pedrycz. 2022. Federated FCM: Clustering under privacy requirements. IEEE Trans. Fuzzy Syst. 30, 8 (2022), 3384\u20133388.","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"e_1_3_3_135_2","first-page":"25","volume-title":"Proceedings of the SIAM SDM","author":"Pensa Ruggero G.","year":"2008","unstructured":"Ruggero G. Pensa and Jean-Fran\u00e7ois Boulicaut. 2008. Constrained co-clustering of gene expression data. In Proceedings of the SIAM SDM. 25\u201336."},{"issue":"1","key":"e_1_3_3_136_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10618-012-0292-8","article-title":"Hierarchical co-clustering: Off-line and incremental approaches","volume":"28","author":"Pensa Ruggero G.","year":"2014","unstructured":"Ruggero G. Pensa, Dino Ienco, and Rosa Meo. 2014. Hierarchical co-clustering: Off-line and incremental approaches. Data Min. Knowl. Discov. 28, 1 (2014), 31\u201364.","journal-title":"Data Min. Knowl. Discov."},{"key":"e_1_3_3_137_2","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.jbi.2015.06.028","article-title":"Biclustering on expression data: A review","volume":"57","author":"Pontes Beatriz","year":"2015","unstructured":"Beatriz Pontes, Ra\u00fal Gir\u00e1ldez, and Jes\u00fas S. Aguilar-Ruiz. 2015. Biclustering on expression data: A review. J. Biomed. Inform. 57 (2015), 163\u2013180.","journal-title":"J. Biomed. Inform."},{"key":"e_1_3_3_138_2","first-page":"991","volume-title":"Proceedings of the ICPR","author":"Qiu Guoping","year":"2004","unstructured":"Guoping Qiu. 2004. Image and feature co-clustering. In Proceedings of the ICPR. 991\u2013994."},{"key":"e_1_3_3_139_2","first-page":"532","volume-title":"Proceedings of the IEEE ICDM","author":"Rege Manjeet","year":"2006","unstructured":"Manjeet Rege, Ming Dong, and Farshad Fotouhi. 2006. Co-clustering documents and words using bipartite isoperimetric graph partitioning. In Proceedings of the IEEE ICDM. 532\u2013541."},{"issue":"5","key":"e_1_3_3_140_2","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1007\/s10994-022-06137-4","article-title":"Semi-supervised latent block model with pairwise constraints","volume":"111","author":"Riverain Paul","year":"2022","unstructured":"Paul Riverain, Simon Fossier, and Mohamed Nadif. 2022. Semi-supervised latent block model with pairwise constraints. Mach. Learn. 111, 5 (2022), 1739\u20131764.","journal-title":"Mach. Learn."},{"issue":"1","key":"e_1_3_3_141_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11634-022-00492-9","article-title":"Poisson degree corrected dynamic stochastic block model","volume":"17","author":"Riverain Paul","year":"2023","unstructured":"Paul Riverain, Simon Fossier, and Mohamed Nadif. 2023. Poisson degree corrected dynamic stochastic block model. Adv. Data Anal. Classif. 17, 1 (2023), 135\u2013162.","journal-title":"Adv. Data Anal. Classif."},{"key":"e_1_3_3_142_2","first-page":"323","volume-title":"Proceedings of the DS (Lecture Notes in Computer Science)","author":"Robardet C\u00e9line","year":"2001","unstructured":"C\u00e9line Robardet and Fabien Feschet. 2001. Efficient local search in conceptual clustering. In Proceedings of the DS (Lecture Notes in Computer Science), Vol. 2226. 323\u2013335."},{"issue":"1","key":"e_1_3_3_143_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1007\/s00357-020-09379-w","article-title":"Comparing high-dimensional partitions with the co-clustering adjusted rand index","volume":"38","author":"Robert Valerie","year":"2021","unstructured":"Valerie Robert, Yann Vasseur, and Vincent Brault. 2021. Comparing high-dimensional partitions with the co-clustering adjusted rand index. J. Classif. 38, 1 (2021), 158\u2013186.","journal-title":"J. Classif."},{"issue":"4","key":"e_1_3_3_144_2","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1016\/j.csda.2007.06.025","article-title":"Two-mode multi-partitioning","volume":"52","author":"Rocci Roberto","year":"2008","unstructured":"Roberto Rocci and Maurizio Vichi. 2008. Two-mode multi-partitioning. Comput. Stat. Data Anal. 52, 4 (2008), 1984\u20132003.","journal-title":"Comput. Stat. 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Classif. 13, 3 (2019), 591\u2013620.","journal-title":"Adv. Data Anal. Classif."},{"key":"e_1_3_3_148_2","first-page":"866","volume-title":"Proceedings of the AISTATS","volume":"51","author":"Salah Aghiles","year":"2016","unstructured":"Aghiles Salah, Nicoleta Rogovschi, and Mohamed Nadif. 2016. Model-based co-clustering for high dimensional sparse data. In Proceedings of the AISTATS, Vol. 51. 866\u2013874."},{"key":"e_1_3_3_149_2","first-page":"3595","article-title":"PAC-Bayesian analysis of co-clustering and beyond","volume":"11","author":"Seldin Yevgeny","year":"2010","unstructured":"Yevgeny Seldin and Naftali Tishby. 2010. PAC-Bayesian analysis of co-clustering and beyond. J. Mach. Learn. Res. 11 (2010), 3595\u20133646.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_3_150_2","doi-asserted-by":"crossref","first-page":"106866","DOI":"10.1016\/j.csda.2019.106866","article-title":"Model-based co-clustering for mixed type data","volume":"144","author":"Selosse Margot","year":"2020","unstructured":"Margot Selosse, Julien Jacques, and Christophe Biernacki. 2020. Model-based co-clustering for mixed type data. Comput. Stat. Data Anal. 144 (2020), 106866.","journal-title":"Comput. Stat. Data Anal."},{"key":"e_1_3_3_151_2","first-page":"792","volume-title":"Proceedings of the ICML","volume":"119","author":"Shashua Amnon","year":"2005","unstructured":"Amnon Shashua and Tamir Hazan. 2005. Non-negative tensor factorization with applications to statistics and computer vision. In Proceedings of the ICML, Vol. 119. 792\u2013799."},{"issue":"2","key":"e_1_3_3_152_2","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1007\/s10618-012-0258-x","article-title":"A survey on enhanced subspace clustering","volume":"26","author":"Sim Kelvin","year":"2013","unstructured":"Kelvin Sim, Vivekanand Gopalkrishnan, Arthur Zimek, and Gao Cong. 2013. A survey on enhanced subspace clustering. Data Min. Knowl. Discov. 26, 2 (2013), 332\u2013397.","journal-title":"Data Min. Knowl. Discov."},{"key":"e_1_3_3_153_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neucom.2018.02.055","article-title":"Model-based co-clustering for functional data","volume":"291","author":"Slimen Yosra Ben","year":"2018","unstructured":"Yosra Ben Slimen, Sylvain Allio, and Julien Jacques. 2018. Model-based co-clustering for functional data. Neurocomputing 291 (2018), 97\u2013108.","journal-title":"Neurocomputing"},{"key":"e_1_3_3_154_2","first-page":"581","volume-title":"Proceedings of the AAAI","author":"Song Yangqiu","year":"2010","unstructured":"Yangqiu Song, Shimei Pan, Shixia Liu, Furu Wei, Michelle X. Zhou, and Weihong Qian. 2010. Constrained coclustering for textual documents. In Proceedings of the AAAI. 581\u2013586."},{"issue":"4","key":"e_1_3_3_155_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3133201","article-title":"Differentially private k-means clustering and a hybrid approach to private optimization","volume":"20","author":"Su Dong","year":"2017","unstructured":"Dong Su, Jianneng Cao, Ninghui Li, Elisa Bertino, Min Lyu, and Hongxia Jin. 2017. Differentially private k-means clustering and a hybrid approach to private optimization. ACM Trans. Privac. Secur. 20, 4 (2017), 1\u201333.","journal-title":"ACM Trans. Privac. Secur."},{"key":"e_1_3_3_156_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.patcog.2017.12.005","article-title":"Feature co-shrinking for co-clustering","volume":"77","author":"Tan Qi","year":"2018","unstructured":"Qi Tan, Pei Yang, and Jingrui He. 2018. Feature co-shrinking for co-clustering. Pattern Recognit. 77 (2018), 12\u201319.","journal-title":"Pattern Recognit."},{"key":"e_1_3_3_157_2","first-page":"700","volume-title":"Proceedings of the SCIS\/ISIS","author":"Tanaka Daiji","year":"2014","unstructured":"Daiji Tanaka, Toshiya Oda, Katsuhiro Honda, and Akira Notsu. 2014. Privacy preserving fuzzy co-clustering with distributed cooccurrence matrices. In Proceedings of the SCIS\/ISIS. 700\u2013705."},{"issue":"3","key":"e_1_3_3_158_2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s10915-021-01489-w","article-title":"Orthogonal dual graph-regularized nonnegative matrix factorization for co-clustering","volume":"87","author":"Tang Jiayi","year":"2021","unstructured":"Jiayi Tang and Zhong Wan. 2021. Orthogonal dual graph-regularized nonnegative matrix factorization for co-clustering. J. Sci. Comput. 87, 3 (2021), 66.","journal-title":"J. Sci. Comput."},{"key":"e_1_3_3_159_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02289464","article-title":"Some mathematical notes on three-mode factor analysis","volume":"31","author":"Tucker Ledyard R.","year":"1966","unstructured":"Ledyard R. Tucker. 1966. Some mathematical notes on three-mode factor analysis. Psychometrika 31 (1966), 279\u2013311.","journal-title":"Psychometrika"},{"key":"e_1_3_3_160_2","volume-title":"Privacy and Data Mining","author":"Vaidya Jaideep","year":"2006","unstructured":"Jaideep Vaidya, Yu Michael Zhu, and Christopher W. Clifton. 2006. Privacy and Data Mining. Springer."},{"issue":"2","key":"e_1_3_3_161_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MSP.2010.939739","article-title":"Subspace clustering","volume":"28","author":"Vidal Rene","year":"2011","unstructured":"Rene Vidal. 2011. Subspace clustering. IEEE Signal Process. Mag. 28, 2 (2011), 52\u201368.","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"e_1_3_3_162_2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","author":"Luxburg Ulrike von","year":"2007","unstructured":"Ulrike von Luxburg. 2007. A tutorial on spectral clustering. Stat. Comput. 17, 4 (2007), 395\u2013416.","journal-title":"Stat. Comput."},{"key":"e_1_3_3_163_2","first-page":"1553","volume-title":"Proceedings of the IJCAI","author":"Wang Hua","year":"2011","unstructured":"Hua Wang, Feiping Nie, Heng Huang, and Fillia Makedon. 2011. Fast nonnegative matrix tri-factorization for large-scale data co-clustering. In Proceedings of the IJCAI, Toby Walsh (Ed.). 1553\u20131558."},{"issue":"7","key":"e_1_3_3_164_2","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1109\/TCYB.2019.2950568","article-title":"Discovering multiple co-clusterings with matrix factorization","volume":"51","author":"Wang Jun","year":"2021","unstructured":"Jun Wang, Xing Wang, Guoxian Yu, Carlotta Domeniconi, Zhiwen Yu, and Zili Zhang. 2021. Discovering multiple co-clusterings with matrix factorization. IEEE Trans. Cybern. 51, 7 (2021), 3576\u20133587.","journal-title":"IEEE Trans. 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Signal Process. 70 (2022), 1625\u20131640.","journal-title":"IEEE Trans. Signal Process."},{"key":"e_1_3_3_168_2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.neucom.2021.08.014","article-title":"Joint nonnegative matrix factorization and network embedding for graph co-clustering","volume":"462","author":"Wang Yan","year":"2021","unstructured":"Yan Wang and Xiaoke Ma. 2021. Joint nonnegative matrix factorization and network embedding for graph co-clustering. Neurocomputing 462 (2021), 453\u2013465.","journal-title":"Neurocomputing"},{"issue":"6","key":"e_1_3_3_169_2","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","article-title":"Nonnegative matrix factorization: A comprehensive review","volume":"25","author":"Wang Yu-Xiong","year":"2013","unstructured":"Yu-Xiong Wang and Yu-Jin Zhang. 2013. Nonnegative matrix factorization: A comprehensive review. IEEE Trans. Knowl. 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Bioinform."},{"key":"e_1_3_3_172_2","first-page":"414","volume-title":"Proceedings of the SIAM SDM","author":"Xu Dongkuan","year":"2019","unstructured":"Dongkuan Xu, Wei Cheng, Bo Zong, Jingchao Ni, Dongjin Song, Wenchao Yu, Yuncong Chen, Haifeng Chen, and Xiang Zhang. 2019. Deep co-clustering. In Proceedings of the SIAM SDM. 414\u2013422."},{"key":"e_1_3_3_173_2","first-page":"379","volume-title":"Proceedings of the AAAI","author":"Xu Peng","year":"2019","unstructured":"Peng Xu, Zhaohong Deng, Kup-Sze Choi, Longbing Cao, and Shitong Wang. 2019. Multi-view information-theoretic co-clustering for co-occurrence data. In Proceedings of the AAAI. 379\u2013386."},{"issue":"3","key":"e_1_3_3_174_2","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu Rui","year":"2005","unstructured":"Rui Xu and Donald C. Wunsch II. 2005. Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 3 (2005), 645\u2013678.","journal-title":"IEEE Trans. Neural Netw."},{"key":"e_1_3_3_175_2","first-page":"267","volume-title":"Proceedings of the ACM SIGIR","author":"Xu Wei","year":"2003","unstructured":"Wei Xu, Xin Liu, and Yihong Gong. 2003. Document clustering based on non-negative matrix factorization. In Proceedings of the ACM SIGIR. 267\u2013273."},{"key":"e_1_3_3_176_2","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","article-title":"Deep multi-view learning methods: A review","volume":"448","author":"Yan Xiaoqiang","year":"2021","unstructured":"Xiaoqiang Yan, Shizhe Hu, Yiqiao Mao, Yangdong Ye, and Hui Yu. 2021. Deep multi-view learning methods: A review. Neurocomputing 448 (2021), 106\u2013129.","journal-title":"Neurocomputing"},{"key":"e_1_3_3_177_2","first-page":"859","volume-title":"Proceedings of the SIGIR","author":"Yang Tianchi","year":"2022","unstructured":"Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li, and Dong Wang. 2022. Co-clustering interactions via attentive hypergraph neural network. In Proceedings of the SIGIR. 859\u2013869."},{"issue":"5","key":"e_1_3_3_178_2","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s11390-015-1585-3","article-title":"Anomaly detection in microblogging via co-clustering","volume":"30","author":"Yang Wu","year":"2015","unstructured":"Wu Yang, Guowei Shen, Wei Wang, Liangyi Gong, Miao Yu, and Guozhong Dong. 2015. Anomaly detection in microblogging via co-clustering. J. Comput. Sci. Technol. 30, 5 (2015), 1097\u20131108.","journal-title":"J. Comput. Sci. Technol."},{"issue":"5","key":"e_1_3_3_179_2","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.ipm.2009.12.007","article-title":"Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds","volume":"46","author":"Yoo Jiho","year":"2010","unstructured":"Jiho Yoo and Seungjin Choi. 2010. Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds. Inf. Process. Manag. 46, 5 (2010), 559\u2013570.","journal-title":"Inf. Process. Manag."},{"key":"e_1_3_3_180_2","doi-asserted-by":"crossref","first-page":"18113","DOI":"10.1109\/ACCESS.2019.2894267","article-title":"Coupled tensor decomposition for user clustering in mobile internet traffic interaction pattern","volume":"7","author":"Yu Ke","year":"2019","unstructured":"Ke Yu, Lifang He, Philip S. Yu, Wenkai Zhang, and Yue Liu. 2019. 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Data Eng."},{"issue":"6","key":"e_1_3_3_182_2","doi-asserted-by":"crossref","first-page":"e1009064","DOI":"10.1371\/journal.pcbi.1009064","article-title":"coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data","volume":"17","author":"Zeng Pengcheng","year":"2021","unstructured":"Pengcheng Zeng and Zhixiang Lin. 2021. coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data. PLoS Comput. Biol. 17, 6 (2021), e1009064.","journal-title":"PLoS Comput. Biol."},{"key":"e_1_3_3_183_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2018.11.019","article-title":"Feature selection with multi-view data: A survey","volume":"50","author":"Zhang Rui","year":"2019","unstructured":"Rui Zhang, Feiping Nie, Xuelong Li, and Xian Wei. 2019. Feature selection with multi-view data: A survey. Inf. 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