{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:03:25Z","timestamp":1750309405838,"version":"3.41.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076188, 62376200, and 62272354"],"award-info":[{"award-number":["62076188, 62376200, and 62272354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Deep clustering is a crucial task in machine learning and data mining that focuses on acquiring feature representations conducive to clustering. Previous research relies on self-supervised representation learning for general feature representations, such features may not be optimally suited for downstream clustering tasks. In this article, we introduce MICCF, a framework designed to bridge this gap and enhance clustering performance. MICCF enhances feature representations by combining mutual information constraints at different levels and employs an auxiliary alignment mutual information module for learning clustering-oriented features. To be specific, we propose a dual mutual information constraints module, incorporating minimal mutual information constraints at the feature level and maximal mutual information constraints at the instance level. This reduction in feature redundancy encourages the neural network to extract more discriminative features, while maximization ensures more unbiased and robust representations. To obtain clustering-oriented representations, the auxiliary alignment mutual information module utilizes pseudo-labels to maximize mutual information through a multi-classifier network, aligning features with the clustering task. The main network and the auxiliary module work in synergy to jointly optimize feature representations that are well-suited for the clustering task. We validate the effectiveness of our method through extensive experiments on six benchmark datasets. The results indicate that our method performs well in most scenarios, particularly on fine-grained datasets, where our approach effectively distinguishes subtle differences between closely related categories. Notably, our approach achieved a remarkable accuracy of 96.4% on the ImageNet-10 dataset, surpassing other comparison methods. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Li-Hyn\/MICCF.git\">https:\/\/github.com\/Li-Hyn\/MICCF.git<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3672402","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T06:25:36Z","timestamp":1720419936000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MICCF: A Mutual Information Constrained Clustering Framework for Learning Clustering-Oriented Feature Representations"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8471-1448","authenticated-orcid":false,"given":"Hongyu","family":"Li","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0542-2280","authenticated-orcid":false,"given":"Lefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, P. R. China and Hubei Luojia Laboratory, Wuhan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1384-9762","authenticated-orcid":false,"given":"Kehua","family":"Su","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-1818","authenticated-orcid":false,"given":"Wei","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","article-title":"Efficient Deep Embedded Subspace Clustering","author":"Cai Jinyu","year":"2022","unstructured":"Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, and Zhao Zhang. 2022. Efficient Deep Embedded Subspace Clustering. In CVPR. 1\u201310.","journal-title":"CVPR"},{"key":"e_1_3_1_3_2","first-page":"139","article-title":"Deep Clustering for Unsupervised Learning of Visual Features","volume":"11218","author":"Caron Mathilde","year":"2018","unstructured":"Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep Clustering for Unsupervised Learning of Visual Features. In ECCV, Vol. 11218. 139\u2013156.","journal-title":"ECCV"},{"key":"e_1_3_1_4_2","first-page":"9912","article-title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","author":"Caron Mathilde","year":"2020","unstructured":"Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. In NeurIPS. 9912\u20139924.","journal-title":"NeurIPS"},{"key":"e_1_3_1_5_2","first-page":"5880","article-title":"Deep Adaptive Image Clustering","author":"Chang Jianlong","year":"2017","unstructured":"Jianlong Chang, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2017. Deep Adaptive Image Clustering. In ICCV. 5880\u20135888.","journal-title":"ICCV"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3035043"},{"key":"e_1_3_1_7_2","first-page":"1597","article-title":"A Simple Framework for Contrastive Learning of Visual Representations","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a. A Simple Framework for Contrastive Learning of Visual Representations. In ICML. 1597\u20131607.","journal-title":"ICML"},{"key":"e_1_3_1_8_2","first-page":"215","article-title":"An Analysis of Single-Layer Networks in Unsupervised Feature Learning","volume":"15","author":"Coates Adam","year":"2011","unstructured":"Adam Coates, Andrew Y. Ng, and Honglak Lee. 2011. An Analysis of Single-Layer Networks in Unsupervised Feature Learning. In AISTATS, Vol. 15. 215\u2013223.","journal-title":"AISTATS"},{"key":"e_1_3_1_9_2","unstructured":"Zhiyuan Dang Cheng Deng Xu Yang and Heng Huang. 2021. Doubly Contrastive Deep Clustering. arXiv:2103.05484. Retrieved from https:\/\/arxiv.org\/abs\/2103.05484"},{"key":"e_1_3_1_10_2","first-page":"248","article-title":"ImageNet: A Large-Scale Hierarchical Image Database","author":"Deng Jia","year":"2009","unstructured":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR. 248\u2013255.","journal-title":"CVPR"},{"key":"e_1_3_1_11_2","first-page":"9908","article-title":"Clustering by Maximizing Mutual Information Across Views","author":"Do Kien","year":"2021","unstructured":"Kien Do, Truyen Tran, and Svetha Venkatesh. 2021. Clustering by Maximizing Mutual Information Across Views. In ICCV. 9908\u20139918.","journal-title":"ICCV"},{"key":"e_1_3_1_12_2","first-page":"1422","article-title":"Unsupervised Visual Representation Learning by Context Prediction","author":"Doersch Carl","year":"2015","unstructured":"Carl Doersch, Abhinav Gupta, and Alexei A. Efros. 2015. Unsupervised Visual Representation Learning by Context Prediction. In ICCV. 1422\u20131430.","journal-title":"ICCV"},{"key":"e_1_3_1_13_2","first-page":"1","article-title":"Adversarial Feature Learning","author":"Donahue Jeff","year":"2017","unstructured":"Jeff Donahue, Philipp Kr\u00e4henb\u00fchl, and Trevor Darrell. 2017. Adversarial Feature Learning. In ICLR. 1\u201318.","journal-title":"ICLR"},{"key":"e_1_3_1_14_2","first-page":"9726","article-title":"Momentum Contrast for Unsupervised Visual Representation Learning","author":"He Kaiming","year":"2020","unstructured":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020. Momentum Contrast for Unsupervised Visual Representation Learning. In CVPR. 9726\u20139735.","journal-title":"CVPR"},{"key":"e_1_3_1_15_2","unstructured":"Geoffrey E. Hinton Oriol Vinyals and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. arXiv:1503.02531. Retrieved from https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"e_1_3_1_16_2","first-page":"1558","article-title":"Learning Discrete Representations via Information Maximizing Self-Augmented Training","volume":"70","author":"Hu Weihua","year":"2017","unstructured":"Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama. 2017. Learning Discrete Representations via Information Maximizing Self-Augmented Training. In ICML, Vol. 70. 1558\u20131567.","journal-title":"ICML"},{"key":"e_1_3_1_17_2","first-page":"8849","article-title":"Deep Semantic Clustering by Partition Confidence Maximisation","author":"Huang Jiabo","year":"2020","unstructured":"Jiabo Huang, Shaogang Gong, and Xiatian Zhu. 2020. Deep Semantic Clustering by Partition Confidence Maximisation. In CVPR. 8849\u20138858.","journal-title":"CVPR"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"e_1_3_1_19_2","first-page":"9865","article-title":"Invariant Information Clustering for Unsupervised Image Classification and Segmentation","author":"Ji Xu","year":"2019","unstructured":"Xu Ji, Andrea Vedaldi, and Jo\u00e3o F. Henriques. 2019. Invariant Information Clustering for Unsupervised Image Classification and Segmentation. In ICCV. 9865\u20139874.","journal-title":"ICCV"},{"issue":"3","key":"e_1_3_1_20_2","first-page":"31:1","article-title":"GRACE: A General Graph Convolution Framework for Attributed Graph Clustering","volume":"17","author":"Kamhoua Barakeel Fanseu","year":"2023","unstructured":"Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, and Bo Han. 2023. GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. ACM Trans. Knowl. Discov. Data 17, 3 (2023), 31:1\u201331.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_1_21_2","first-page":"18661","article-title":"Supervised Contrastive Learning","author":"Khosla Prannay","year":"2020","unstructured":"Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised Contrastive Learning. In NeurIPS. 18661\u201318673.","journal-title":"NeurIPS"},{"key":"e_1_3_1_22_2","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1312.6114 arXiv:1312.6114."},{"key":"e_1_3_1_23_2","first-page":"1","article-title":"A Mutual Information Maximization Perspective of Language Representation Learning","author":"Kong Lingpeng","year":"2020","unstructured":"Lingpeng Kong, Cyprien de Masson d\u2019Autume, Lei Yu, Wang Ling, Zihang Dai, and Dani Yogatama. 2020. A Mutual Information Maximization Perspective of Language Representation Learning. In ICLR. 1\u201312.","journal-title":"ICLR"},{"key":"e_1_3_1_24_2","volume-title":"Learning Multiple Layers of Features From Tiny Images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning Multiple Layers of Features From Tiny Images. Technical Report. CIFAR."},{"key":"e_1_3_1_25_2","first-page":"2539","article-title":"Dual Mutual Information Constraints for Discriminative Clustering","author":"Li H.","year":"2023","unstructured":"H. Li, L. Zhang, and K. Su. 2023. Dual Mutual Information Constraints for Discriminative Clustering. In AAAI. 2539\u20132545.","journal-title":"AAAI"},{"key":"e_1_3_1_26_2","first-page":"362","article-title":"The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering","author":"Li Tao","year":"2006","unstructured":"Tao Li and Chris Ding. 2006. The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering. In ICDM. 362\u2013371.","journal-title":"ICDM"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17037"},{"key":"e_1_3_1_28_2","first-page":"4554","article-title":"Pseudo Supervised Matrix Factorization in Discriminative Subspace","author":"Ma Jiaqi","year":"2019","unstructured":"Jiaqi Ma, Yipeng Zhang, Lefei Zhang, Bo Du, and Dapeng Tao. 2019. Pseudo Supervised Matrix Factorization in Discriminative Subspace. In IJCAI. 4554\u20134560.","journal-title":"IJCAI"},{"key":"e_1_3_1_29_2","first-page":"2539","article-title":"Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering","author":"Ma Sihan","year":"2018","unstructured":"Sihan Ma, Lefei Zhang, Wenbin Hu, Yipeng Zhang, Jia Wu, and Xuelong Li. 2018. Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering. In IJCAI. 2539\u20132545.","journal-title":"IJCAI"},{"key":"e_1_3_1_30_2","first-page":"281","article-title":"Some Methods for Classification and Analysis of Multivariate Observations","volume":"1","author":"MacQueen J.","year":"1967","unstructured":"J. MacQueen. 1967. Some Methods for Classification and Analysis of Multivariate Observations. In BSMSP, Vol. 1. 281\u2013297.","journal-title":"BSMSP"},{"key":"e_1_3_1_31_2","first-page":"3418","article-title":"DivClust: Controlling Diversity in Deep Clustering","author":"Metaxas Ioannis Maniadis","year":"2023","unstructured":"Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, and Ioannis Patras. 2023. DivClust: Controlling Diversity in Deep Clustering. In CVPR. 3418\u20133428.","journal-title":"CVPR"},{"key":"e_1_3_1_32_2","first-page":"1969","article-title":"The Constrained Laplacian Rank Algorithm for Graph-Based Clustering","author":"Nie Feiping","year":"2016","unstructured":"Feiping Nie, Xiaoqian Wang, Michael I. Jordan, and Heng Huang. 2016. The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. In AAAI. 1969\u20131976.","journal-title":"AAAI"},{"key":"e_1_3_1_33_2","first-page":"69","article-title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","author":"Noroozi Mehdi","year":"2016","unstructured":"Mehdi Noroozi and Paolo Favaro. 2016. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. In ECCV. 69\u201384.","journal-title":"ECCV"},{"key":"e_1_3_1_34_2","first-page":"464","article-title":"Deep Fuzzy Clustering with Weighted Intra-Class Variance and Extended Mutual Information Regularization","author":"Pang Yunsheng","year":"2020","unstructured":"Yunsheng Pang, Feiyu Chen, Sheng Huang, Yongxin Ge, Wei Wang, and Taiping Zhang. 2020. Deep Fuzzy Clustering with Weighted Intra-Class Variance and Extended Mutual Information Regularization. In ICDM. 464\u2013471.","journal-title":"ICDM"},{"key":"e_1_3_1_35_2","first-page":"2701","article-title":"Learning Features by Watching Objects Move","author":"Pathak Deepak","year":"2017","unstructured":"Deepak Pathak, Ross Girshick, Piotr Doll\u00e1r, Trevor Darrell, and Bharath Hariharan. 2017. Learning Features by Watching Objects Move. In CVPR. 2701\u20132710.","journal-title":"CVPR"},{"key":"e_1_3_1_36_2","first-page":"830","article-title":"Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies","volume":"12084","author":"Roy Puloma","year":"2020","unstructured":"Puloma Roy, Sadia Sharmin, Amin Ahsan Ali, and Mohammad Shoyaib. 2020. Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies. In PAKDD, Vol. 12084. 830\u2013842.","journal-title":"PAKDD"},{"key":"e_1_3_1_37_2","first-page":"6490","article-title":"Finegan: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery","author":"Singh Krishna Kumar","year":"2019","unstructured":"Krishna Kumar Singh, Utkarsh Ojha, and Yong Jae Lee. 2019. Finegan: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery. In CVPR. 6490\u20136499.","journal-title":"CVPR"},{"key":"e_1_3_1_38_2","first-page":"583","article-title":"Cluster Ensembles\u2014A Knowledge Reuse Framework for Combining Multiple Partitions","author":"Strehl Alexander","year":"2002","unstructured":"Alexander Strehl and Joydeep Ghosh. 2002. Cluster Ensembles\u2014A Knowledge Reuse Framework for Combining Multiple Partitions. In JMLR. 583\u2013617.","journal-title":"JMLR"},{"key":"e_1_3_1_39_2","first-page":"1","article-title":"Clustering-Friendly Representation Learning via Instance Discrimination and Feature Decorrelation","author":"Tao Yaling","year":"2021","unstructured":"Yaling Tao, Kentaro Takagi, and Kouta Nakata. 2021. Clustering-Friendly Representation Learning via Instance Discrimination and Feature Decorrelation. In ICLR. 1\u201315.","journal-title":"ICLR"},{"key":"e_1_3_1_40_2","first-page":"268","article-title":"Scan: Learning to Classify Images Without Labels","author":"Gansbeke Wouter Van","year":"2020","unstructured":"Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. 2020. Scan: Learning to Classify Images Without Labels. In ECCV. 268\u2013285.","journal-title":"ECCV"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3584862"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3623400"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17231"},{"key":"e_1_3_1_44_2","first-page":"8149","article-title":"Deep Comprehensive Correlation Mining for Image Clustering","author":"Wu Jianlong","year":"2019","unstructured":"Jianlong Wu, Keyu Long, Fei Wang, Chen Qian, Cheng Li, Zhouchen Lin, and Hongbin Zha. 2019. Deep Comprehensive Correlation Mining for Image Clustering. In ICCV. 8149\u20138158.","journal-title":"ICCV"},{"key":"e_1_3_1_45_2","first-page":"3733","article-title":"Unsupervised Feature Learning via Non-Parametric Instance Discrimination","author":"Wu Zhirong","year":"2018","unstructured":"Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. 2018. Unsupervised Feature Learning via Non-Parametric Instance Discrimination. In CVPR. 3733\u20133742.","journal-title":"CVPR"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979685"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110065"},{"key":"e_1_3_1_48_2","first-page":"5147","article-title":"Joint Unsupervised Learning of Deep Representations and Image Clusters","author":"Yang Jianwei","year":"2016","unstructured":"Jianwei Yang, Devi Parikh, and Dhruv Batra. 2016. Joint Unsupervised Learning of Deep Representations and Image Clusters. In CVPR. 5147\u20135156.","journal-title":"CVPR"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.09.142"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589768"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3025665"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110032"},{"key":"e_1_3_1_53_2","unstructured":"Huasong Zhong Chong Chen Zhongming Jin and Xian-Sheng Hua. 2020. Deep Robust Clustering by Contrastive Learning. arXiv:2008.03030. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2008.03030"},{"key":"e_1_3_1_54_2","first-page":"627","article-title":"Contrastive Hierarchical Clustering","author":"Znalezniak Micha\u0142","year":"2023","unstructured":"Micha\u0142 Znalezniak, Przemys\u0142aw Rola, Patryk Kaszuba, Jacek Tabor, and Marek \u015bmieja. 2023. Contrastive Hierarchical Clustering. In ECML-PKDD. 627\u2013643.","journal-title":"ECML-PKDD"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672402","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3672402","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:00Z","timestamp":1750294680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":53,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3672402"],"URL":"https:\/\/doi.org\/10.1145\/3672402","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2024,8,16]]},"assertion":[{"value":"2023-12-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}