{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:28:00Z","timestamp":1762352880552,"version":"3.41.0"},"reference-count":101,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,2,29]]},"abstract":"<jats:p>\n            There is a large body of works on multi-view clustering that exploit multiple representations (or views) of the same input data for better convergence. These multiple views can come from multiple modalities (image, audio, text) or different feature subsets. Obtaining one consensus partitioning after considering different views is usually a non-trivial task. Recently, multi-objective based multi-view clustering methods have suppressed the performance of single objective based multi-view clustering techniques. One key problem is that it is difficult to select a single solution from a set of alternative partitionings generated by multi-objective techniques on the final Pareto optimal front. In this article, we propose a novel multi-objective based multi-view clustering framework that overcomes the problem of selecting a single solution in multi-objective based techniques. In particular, our proposed framework has three major components as follows: (i) multi-view based multi-objective algorithm, Multiview-AMOSA, for initial clustering of data points; (ii) a generative model for generating a combined solution having probabilistic labels; and (iii)\n            <jats:italic>K<\/jats:italic>\n            -means algorithm for obtaining the final labels. As the first component, we have adopted a recently developed multi-view based multi-objective clustering algorithm to generate different possible consensus partitionings of a given dataset taking into account different views. A generative model is coupled with the first component to generate a single consensus partitioning after considering multiple solutions. It exploits the latent subsets of the non-dominated solutions obtained from the multi-objective clustering algorithm and combines them to produce a single probabilistic labeled solution. Finally, a simple clustering algorithm, namely\n            <jats:italic>K<\/jats:italic>\n            -means, is applied on the generated probabilistic labels to obtain the final cluster labels. Experimental validation of our proposed framework is carried out over several benchmark datasets belonging to three different domains; UCI datasets, multi-view datasets, search result clustering datasets, and patient stratification datasets. Experimental results show that our proposed framework achieves an improvement of around 2%--4% over different evaluation metrics in all the four domains in comparison to state-of-the art methods.\n          <\/jats:p>","DOI":"10.1145\/3365673","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T14:54:43Z","timestamp":1580741683000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative Model"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8140-6499","authenticated-orcid":false,"given":"Sayantan","family":"Mitra","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Patna, Bihar, India"}]},{"given":"Mohammed","family":"Hasanuzzaman","sequence":"additional","affiliation":[{"name":"ADAPT Centre, School of Computing, Dublin, Ireland"}]},{"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, Bihar, India"}]}],"member":"320","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"volume-title":"Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers (COLING\u201914)","year":"2014","author":"Acharya Sudipta","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-008-9066-8"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.11.012"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.892604"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm418"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.03.026"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2007.900837"},{"key":"e_1_2_1_8_1","first-page":"3","article-title":"A simulated annealing based multi-objective optimization algorithm","volume":"12","author":"Bandyopadhyay S.","year":"2008","journal-title":"AMOSA. IEEE Transactions on Evolutionary Computation"},{"volume-title":"Functional Genomics","author":"Ben-Hur Asa","key":"e_1_2_1_9_1"},{"volume-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"Bezdek J. C.","key":"e_1_2_1_10_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"e_1_2_1_11_1","first-page":"19","article-title":"Multi-view clustering","volume":"4","author":"Bickel Steffen","year":"2004","journal-title":"Proceedings of ICDM"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.08.024"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-11-0489"},{"volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI\u201913)","year":"2013","author":"Cai Xiao","key":"e_1_2_1_14_1"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541884"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835480"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553391"},{"volume-title":"Proceedings of the Workshops at the 31st AAAI Conference on Artificial Intelligence.","year":"2017","author":"Chen Dongdong","key":"e_1_2_1_18_1"},{"key":"e_1_2_1_19_1","unstructured":"Gang Chen. 2015. Deep learning with nonparametric clustering. arxiv:1501.03084. Retrieved from http:\/\/arxiv.org\/abs\/1501.03084.  Gang Chen. 2015. Deep learning with nonparametric clustering. arxiv:1501.03084. Retrieved from http:\/\/arxiv.org\/abs\/1501.03084."},{"volume-title":"Proceedings of the NIPS.","year":"2016","author":"Chen Xi","key":"e_1_2_1_20_1"},{"volume-title":"Multi-view clustering via spectral partitioning and local refinement. Information Processing 8 Management 52, 4","year":"2016","author":"Chikhi Nacim Fateh","key":"e_1_2_1_21_1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/WI.2005.75"},{"volume-title":"Proceedings of the ICML Workshop on Learning with Multiple Views. 20--27","year":"2005","author":"De Sa Virginia R.","key":"e_1_2_1_23_1"},{"volume-title":"Multi-objective Optimization Using Evolutionary Algorithms","author":"Deb K.","key":"e_1_2_1_24_1"},{"key":"e_1_2_1_25_1","unstructured":"Nat Dilokthanakul Pedro A. M. Mediano Marta Garnelo Matthew C. H. Lee Hugh Salimbeni Kai Arulkumaran and Murray Shanahan. 2016. Deep unsupervised clustering with Gaussian mixture variational autoencoders. arxiv:1611.02648. Retrieved from http:\/\/arxiv.org\/abs\/1611.02648  Nat Dilokthanakul Pedro A. M. Mediano Marta Garnelo Matthew C. H. Lee Hugh Salimbeni Kai Arulkumaran and Murray Shanahan. 2016. Deep unsupervised clustering with Gaussian mixture variational autoencoders. arxiv:1611.02648. Retrieved from http:\/\/arxiv.org\/abs\/1611.02648"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg038"},{"key":"e_1_2_1_27_1","unstructured":"M. Everingham L. Van Gool C. K. I. Williams J. Winn and A. Zisserman. 2007. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. Retrieved from http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html.  M. Everingham L. Van Gool C. K. I. Williams J. Winn and A. Zisserman. 2007. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. Retrieved from http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/1367012.1367014"},{"volume-title":"Andre CPLF de Carvalho, and Marcilio CP de Souto","year":"2008","author":"Faceli Katti","key":"e_1_2_1_29_1"},{"volume-title":"Proceedings of the 21st International Conference on Machine Learning (ICML\u201904)","author":"Fern Xiaoli Zhang","key":"e_1_2_1_30_1"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.113"},{"key":"e_1_2_1_32_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672--2680."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.877146"},{"volume-title":"Classification \u2014 The Ubiquitous Challenge","author":"Hornik Kurt","key":"e_1_2_1_34_1"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.08.015"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-014-0307-6"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2016.7744208"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172161"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1963.10500845"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"volume-title":"Kingma and Max Welling","year":"2013","author":"Diederik","key":"e_1_2_1_42_1"},{"volume-title":"Proceedings of the 28th International Conference on Machine Learning (ICML\u201911)","year":"2011","author":"Kumar Abhishek","key":"e_1_2_1_43_1"},{"volume-title":"Advances in Neural Information Processing Systems 24","author":"Kumar Abhishek","key":"e_1_2_1_44_1"},{"volume-title":"Journal of Machine Learning Research","year":"2009","author":"Langford John","key":"e_1_2_1_45_1"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/298"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.28"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972788.74"},{"key":"e_1_2_1_49_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781.  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2011.129"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2855437"},{"volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 3793--3805","year":"2018","author":"Mitra Sayantan","key":"e_1_2_1_52_1"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0216904"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.06.059"},{"key":"e_1_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Q. Mo R. Shen C. Guo M. Vannucci K. S. Chan and S. G. Hilsenbeck. 2018. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics (Oxford England) 19 1 (2018) 71--86.  Q. Mo R. Shen C. Guo M. Vannucci K. S. Chan and S. G. Hilsenbeck. 2018. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics (Oxford England) 19 1 (2018) 71--86.","DOI":"10.1093\/biostatistics\/kxx017"},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","volume":"2","author":"Moreno Jos\u00e9 G.","year":"2013"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2008182"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAPR.2009.51"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2012163"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.5555\/1870658.1870670"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.215129.116"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/357"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2005.38"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2003.06.005"},{"volume-title":"Proceedings of the 3rd Annual Conference on Genetic Programming","author":"Park Y. J.","key":"e_1_2_1_65_1"},{"volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 3567--3575","year":"2016","author":"Ratner Alexander J.","key":"e_1_2_1_66_1"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/367"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.5555\/3225669.3226015"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.07.004"},{"volume-title":"Article 44 (May","year":"2018","author":"Saha Sriparna","key":"e_1_2_1_71_1"},{"volume-title":"Article 44 (May","year":"2018","author":"Saha Sriparna","key":"e_1_2_1_72_1"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-015-0680-3"},{"key":"e_1_2_1_74_1","unstructured":"Sohil Atul Shah and Vladlen Koltun. 2018. Deep continuous clustering. arXiv:1803.01449.  Sohil Atul Shah and Vladlen Koltun. 2018. Deep continuous clustering. arXiv:1803.01449."},{"volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR'16)","year":"2015","author":"Springenberg Jost Tobias","key":"e_1_2_1_75_1"},{"key":"e_1_2_1_76_1","first-page":"583","article-title":"Cluster ensembles\u2014A knowledge reuse framework for combining multiple partitions","author":"Strehl Alexander","year":"2002","journal-title":"Journal of Machine Learning Research 3"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.017"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1362-6"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2007.05.010"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04277-5_21"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2014.6900586"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2015.7257091"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.26"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.04.002"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-015-2223-8"},{"volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence.","year":"2014","author":"Xia Rongkai","key":"e_1_2_1_87_1"},{"volume-title":"Proceedings of the International Conference on Machine Learning. 478--487","year":"2016","author":"Xie Junyuan","key":"e_1_2_1_88_1"},{"key":"e_1_2_1_89_1","volume-title":"Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC\u201913)","volume":"1","author":"Xie Xijiong","year":"2013"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.85677"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.578"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2665976"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2011.65"},{"volume-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70","year":"2017","author":"Yang Bo","key":"e_1_2_1_94_1"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/290941.290956"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2877660"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.461"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2733001"},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020593"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.04.004"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2005.11.003"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.02.003"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3365673","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3365673","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:36Z","timestamp":1750202616000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3365673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":101,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,2,29]]}},"alternative-id":["10.1145\/3365673"],"URL":"https:\/\/doi.org\/10.1145\/3365673","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2020,2,3]]},"assertion":[{"value":"2018-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}