{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:14Z","timestamp":1786120814156,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":84,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62376236"],"award-info":[{"award-number":["62376236"]}]},{"name":"General Program of Natural Science Foundation of Guangdong Province","award":["2024A1515011771"],"award-info":[{"award-number":["2024A1515011771"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3817483","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:47Z","timestamp":1786036547000},"page":"10044-10055","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CLUBench: A Clustering Benchmark"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1410-4295","authenticated-orcid":false,"given":"Feng","family":"Xiao","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4752-3835","authenticated-orcid":false,"given":"Dazhi","family":"Fu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3374-1941","authenticated-orcid":false,"given":"Chris","family":"Ding","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9665-0355","authenticated-orcid":false,"given":"Jicong","family":"Fan","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1055558.1055581"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671888"},{"key":"e_1_3_2_1_3_1","unstructured":"AI@Meta. 2024. Llama 3 Model Card. (2024). https:\/\/github.com\/meta-llama\/llama3\/blob\/main\/MODEL_CARD.md"},{"key":"e_1_3_2_1_4_1","volume-title":"Clustering with deep learning: Taxonomy and new methods. arXiv preprint arXiv:1801.07648","author":"Aljalbout Elie","year":"2018","unstructured":"Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, and Daniel Cremers. 2018. Clustering with deep learning: Taxonomy and new methods. arXiv preprint arXiv:1801.07648 (2018)."},{"key":"e_1_3_2_1_5_1","volume-title":"OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record","author":"Ankerst Mihael","year":"1999","unstructured":"Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, and J\u00f6rg Sander. 1999. OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record, Vol. 28, 2 (1999), 49-60."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1270"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065708001518"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014062"},{"key":"e_1_3_2_1_9_1","volume-title":"Online clustering of parallel data streams. Data & knowledge engineering","author":"Beringer J\u00fcrgen","year":"2006","unstructured":"J\u00fcrgen Beringer and Eyke H\u00fcllermeier. 2006. Online clustering of parallel data streams. Data & knowledge engineering, Vol. 58, 2 (2006), 180-204."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28349-8_2"},{"key":"e_1_3_2_1_11_1","volume-title":"Random forests. Machine learning","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman. 2001. Random forests. Machine learning, Vol. 45, 1 (2001), 5-32."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00012"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.02.088"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2184319.2184343"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01536"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00421"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2018.2832197"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"e_1_3_2_1_20_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014118"},{"key":"e_1_3_2_1_22_1","volume-title":"Sparse subspace clustering: Algorithm, theory, and applications","author":"Elhamifar Ehsan","year":"2013","unstructured":"Ehsan Elhamifar and Ren\u00e9 Vidal. 2013. Sparse subspace clustering: Algorithm, theory, and applications. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 11 (2013), 2765-2781."},{"key":"e_1_3_2_1_23_1","unstructured":"Martin Ester Hans-Peter Kriegel J\u00f6rg Sander Xiaowei Xu et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd Vol. 96. 226-231."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467267"},{"key":"e_1_3_2_1_25_1","volume-title":"Factor group-sparse regularization for efficient low-rank matrix recovery. Advances in neural information processing Systems","author":"Fan Jicong","year":"2019","unstructured":"Jicong Fan, Lijun Ding, Yudong Chen, and Madeleine Udell. 2019. Factor group-sparse regularization for efficient low-rank matrix recovery. Advances in neural information processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0720"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3270311"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1480"},{"key":"e_1_3_2_1_29_1","volume-title":"Cluster analysis of multivariate data: efficiency versus interpretability of classifications. biometrics","author":"Forgy Edward W","year":"1965","unstructured":"Edward W Forgy. 1965. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. biometrics, Vol. 21 (1965), 768-769."},{"key":"e_1_3_2_1_30_1","first-page":"1753","article-title":"Improved deep embedded clustering with local structure preservation","volume":"17","author":"Guo Xifeng","year":"2017","unstructured":"Xifeng Guo, Long Gao, Xinwang Liu, and Jianping Yin. 2017. Improved deep embedded clustering with local structure preservation.. In Ijcai, Vol. 17. 1753-1759.","journal-title":"Ijcai"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICICEE.2012.303"},{"key":"e_1_3_2_1_32_1","volume-title":"Cluster analysis and mathematical programming. Mathematical programming","author":"Hansen Pierre","year":"1997","unstructured":"Pierre Hansen and Brigitte Jaumard. 1997. Cluster analysis and mathematical programming. Mathematical programming, Vol. 79, 1 (1997), 191-215."},{"key":"e_1_3_2_1_33_1","volume-title":"Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385","author":"He Kaiming","year":"2015","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385 (2015)."},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Learning Representations","author":"Hollmann Noah","year":"2023","unstructured":"Noah Hollmann, Samuel M\u00fcller, Katharina Eggensperger, and Frank Hutter. 2023. TabPFN: A transformer that solves small tabular classification problems in a second. In International Conference on Learning Representations 2023."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08328-6"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00887"},{"key":"e_1_3_2_1_37_1","volume-title":"Data clustering: a review. ACM computing surveys (CSUR)","author":"Jain Anil K","year":"1999","unstructured":"Anil K Jain, M Narasimha Murty, and Patrick J Flynn. 1999. Data clustering: a review. ACM computing surveys (CSUR), Vol. 31, 3 (1999), 264-323."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2020.100001"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3548011"},{"key":"e_1_3_2_1_40_1","volume-title":"Deep subspace clustering networks. Advances in neural information processing systems","author":"Ji Pan","year":"2017","unstructured":"Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, and Ian Reid. 2017. Deep subspace clustering networks. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289588"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5867"},{"key":"e_1_3_2_1_43_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.6026\/97320630002005"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW60847.2023.00087"},{"key":"e_1_3_2_1_46_1","volume-title":"You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering. arXiv preprint arXiv:2511.21193","author":"Li Hanyang","year":"2025","unstructured":"Hanyang Li, Yuheng Jia, Hui Liu, and Junhui Hou. 2025b. You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering. arXiv preprint arXiv:2511.21193 (2025)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26032"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17037"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01639-z"},{"key":"e_1_3_2_1_50_1","volume-title":"Forty-second International Conference on Machine Learning.","author":"Li Zhixin","year":"2025","unstructured":"Zhixin Li, Yuheng Jia, Junhui Hou, et al., 2025a. Learning from Sample Stability for Deep Clustering. In Forty-second International Conference on Machine Learning."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709207"},{"key":"e_1_3_2_1_52_1","volume-title":"Robust recovery of subspace structures by low-rank representation","author":"Liu Guangcan","year":"2012","unstructured":"Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. 2012. Robust recovery of subspace structures by low-rank representation. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 1 (2012), 171-184."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3198638"},{"key":"e_1_3_2_1_54_1","volume-title":"A survey of deep graph clustering: Taxonomy, challenge, application, and open resource. arXiv preprint arXiv:2211.12875","author":"Liu Yue","year":"2022","unstructured":"Yue Liu, Jun Xia, Sihang Zhou, Xihong Yang, Ke Liang, Chenchen Fan, Yan Zhuang, Stan Z Li, Xinwang Liu, and Kunlun He. 2022. A survey of deep graph clustering: Taxonomy, challenge, application, and open resource. arXiv preprint arXiv:2211.12875 (2022)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3271871"},{"key":"e_1_3_2_1_56_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, Nov (2008), 2579-2605.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_57_1","volume-title":"Proc. of 5th Berkeley Symposium on Math. Stat. and Prob., 281-297","author":"McQueen James B","year":"1967","unstructured":"James B McQueen. 1967. Some methods of classification and analysis of multivariate observations. In Proc. of 5th Berkeley Symposium on Math. Stat. and Prob., 281-297."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00333"},{"key":"e_1_3_2_1_59_1","volume-title":"A survey of clustering with deep learning: From the perspective of network architecture","author":"Min Erxue","year":"2018","unstructured":"Erxue Min, Xifeng Guo, Qiang Liu, Gen Zhang, Jianjing Cui, and Jun Long. 2018. A survey of clustering with deep learning: From the perspective of network architecture. IEEE access, Vol. 6 (2018), 39501-39514."},{"key":"e_1_3_2_1_60_1","volume-title":"Algorithms for hierarchical clustering: an overview","author":"Murtagh Fionn","year":"2012","unstructured":"Fionn Murtagh and Pedro Contreras. 2012. Algorithms for hierarchical clustering: an overview. Wiley interdisciplinary reviews: data mining and knowledge discovery, Vol. 2, 1 (2012), 86-97."},{"key":"e_1_3_2_1_61_1","volume-title":"Ng and Jiawei Han","author":"Raymond","year":"2002","unstructured":"Raymond T. Ng and Jiawei Han. 2002. CLARANS: A method for clustering objects for spatial data mining. IEEE transactions on knowledge and data engineering, Vol. 14, 5 (2002), 1003-1016."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-97864-2_4"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.5555\/1368018.1368020"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-025-02548-8"},{"key":"e_1_3_2_1_65_1","volume-title":"A simple and fast algorithm for K-medoids clustering. Expert systems with applications","author":"Park Hae-Sang","year":"2009","unstructured":"Hae-Sang Park and Chi-Hyuck Jun. 2009. A simple and fast algorithm for K-medoids clustering. Expert systems with applications, Vol. 36, 2 (2009), 3336-3341."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2012.10.003"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2551"},{"key":"e_1_3_2_1_68_1","volume-title":"International conference on machine learning. PmLR, 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748-8763."},{"key":"e_1_3_2_1_69_1","volume-title":"The infinite Gaussian mixture model. Advances in neural information processing systems","author":"Rasmussen Carl","year":"1999","unstructured":"Carl Rasmussen. 1999. The infinite Gaussian mixture model. Advances in neural information processing systems, Vol. 12 (1999)."},{"key":"e_1_3_2_1_70_1","volume-title":"Deep clustering: A comprehensive survey","author":"Ren Yazhou","year":"2024","unstructured":"Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S Yu, and Lifang He. 2024. Deep clustering: A comprehensive survey. IEEE transactions on neural networks and learning systems, Vol. 36, 4 (2024), 5858-5878."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3137369"},{"key":"e_1_3_2_1_72_1","volume-title":"Clustering benchmark datasets exploiting the fundamental clustering problems. Data in brief","author":"Thrun Michael C","year":"2020","unstructured":"Michael C Thrun and Alfred Ultsch. 2020. Clustering benchmark datasets exploiting the fundamental clustering problems. Data in brief, Vol. 30 (2020), 105501."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8916"},{"key":"e_1_3_2_1_74_1","volume-title":"Meta-learning: A survey. arXiv preprint arXiv:1810.03548","author":"Vanschoren Joaquin","year":"2018","unstructured":"Joaquin Vanschoren. 2018. Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018)."},{"key":"e_1_3_2_1_75_1","volume-title":"A tutorial on spectral clustering. Statistics and computing","author":"Luxburg Ulrike Von","year":"2007","unstructured":"Ulrike Von Luxburg. 2007. A tutorial on spectral clustering. Statistics and computing, Vol. 17, 4 (2007), 395-416."},{"key":"e_1_3_2_1_76_1","first-page":"235","volume-title":"Foundations and Trends\u00ae in Machine Learning","volume":"2","author":"Von Ulrike","year":"2010","unstructured":"Ulrike Von Luxburg et al., 2010. Clustering stability: an overview. Foundations and Trends\u00ae in Machine Learning, Vol. 2, 3 (2010), 235-274."},{"key":"e_1_3_2_1_77_1","first-page":"187","volume-title":"Proceedings of the 19th conference on natural language processing (konvens","author":"Wehrli Silvan","year":"2023","unstructured":"Silvan Wehrli, Bert Arnrich, and Christopher Irrgang. 2023. German text embedding clustering benchmark. In Proceedings of the 19th conference on natural language processing (konvens 2023). 187-201."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127761"},{"key":"e_1_3_2_1_79_1","volume-title":"International conference on machine learning. PMLR, 478-487","author":"Xie Junyuan","year":"2016","unstructured":"Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International conference on machine learning. PMLR, 478-487."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-015-0040-1"},{"key":"e_1_3_2_1_81_1","volume-title":"scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing. arXiv preprint arXiv:2512.02471","author":"Xu Ping","year":"2025","unstructured":"Ping Xu, Zaitian Wang, Zhirui Wang, Pengjiang Li, Jiajia Wang, Ran Zhang, Pengfei Wang, and Yuanchun Zhou. 2025. scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing. arXiv preprint arXiv:2512.02471 (2025)."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.845141"},{"key":"e_1_3_2_1_83_1","volume-title":"A rapid review of clustering algorithms. arXiv preprint arXiv:2401.07389","author":"Yin Hui","year":"2024","unstructured":"Hui Yin, Amir Aryani, Stephen Petrie, Aishwarya Nambissan, Aland Astudillo, and Shengyuan Cao. 2024. A rapid review of clustering algorithms. arXiv preprint arXiv:2401.07389 (2024)."},{"key":"e_1_3_2_1_84_1","volume-title":"P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering. arXiv preprint arXiv:2401.09266","author":"Zhang Chuyu","year":"2024","unstructured":"Chuyu Zhang, Hui Ren, and Xuming He. 2024. P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering. arXiv preprint arXiv:2401.09266 (2024)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:31:32Z","timestamp":1786066292000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3817483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":84,"alternative-id":["10.1145\/3770855.3817483","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3817483","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}