{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:22:00Z","timestamp":1779218520096,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02447-2","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T09:30:33Z","timestamp":1731576633000},"page":"3253-3268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing multi-label disease diagnosis through hypergraph clustering and multi-classification label entropy"],"prefix":"10.1007","volume":"16","author":[{"given":"Wenyang","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"issue":"2","key":"2447_CR1","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1016\/j.eswa.2011.06.056","volume":"39","author":"E Alvares-Cherman","year":"2012","unstructured":"Alvares-Cherman E, Metz J, Monard MC (2012) Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Syst Appl 39(2):1647\u20131655","journal-title":"Expert Syst Appl"},{"key":"2447_CR2","unstructured":"Arya D, Gupta DK, Rudinac S et al (2020) Hypersage: generalizing inductive representation learning on hypergraphs. arXiv preprint arXiv:2010.04558"},{"issue":"1","key":"2447_CR3","doi-asserted-by":"publisher","first-page":"07","DOI":"10.30871\/jaic.v6i1.3485","volume":"6","author":"IF Ashari","year":"2022","unstructured":"Ashari IF, Banjarnahor R, Farida DR et al (2022) Application of data mining with the K-means clustering method and Davies Bouldin index for grouping IMDB movies. J Appl Inform Comput 6(1):07\u201315","journal-title":"J Appl Inform Comput"},{"key":"2447_CR4","doi-asserted-by":"crossref","unstructured":"Bo D, Wang X, Shi C et al (2020) Structural deep clustering network. In: Proceedings of the web conference 2020, pp 1400\u20131410","DOI":"10.1145\/3366423.3380214"},{"issue":"1","key":"2447_CR5","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1109\/JAS.2017.7510781","volume":"5","author":"Z Cai","year":"2017","unstructured":"Cai Z, Zhu W (2017) Feature selection for multi-label classification using neighborhood preservation. IEEE\/CAA J Autom Sin 5(1):320\u2013330","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"1","key":"2447_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.imr.2023.101019","volume":"13","author":"Z Chen","year":"2024","unstructured":"Chen Z, Zhang D, Liu C et al (2024) Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning. Integr Med Res 13(1):101019","journal-title":"Integr Med Res"},{"key":"2447_CR7","unstructured":"Devlin J, Chang MW, Lee K et al (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"2447_CR8","doi-asserted-by":"crossref","unstructured":"Feng Y, You H, Zhang Z et al (2019) Hypergraph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 3558\u20133565","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"2447_CR9","doi-asserted-by":"crossref","unstructured":"Fu A, Ma J, Wang C et al (2022) Traditional Chinese medicine health status identification with graph attention network. In: International conference on machine learning for cyber security. Springer, pp 1\u201314","DOI":"10.1007\/978-3-031-20096-0_1"},{"key":"2447_CR10","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, vol 30"},{"issue":"8","key":"2447_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"2447_CR12","doi-asserted-by":"crossref","unstructured":"Huang J, Yang J (2021) Unignn: a unified framework for graph and hypergraph neural networks. arXiv preprint arXiv:2105.00956","DOI":"10.24963\/ijcai.2021\/353"},{"key":"2447_CR13","doi-asserted-by":"crossref","unstructured":"Ji G, He S, Xu L et al (2015) Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, pp 687\u2013696","DOI":"10.3115\/v1\/P15-1067"},{"key":"2447_CR14","doi-asserted-by":"crossref","unstructured":"Jin Y, Zhang W, He X et al (2020) Syndrome-aware herb recommendation with multi-graph convolution network. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, pp 145\u2013156","DOI":"10.1109\/ICDE48307.2020.00020"},{"key":"2447_CR15","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"2447_CR16","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"2447_CR17","volume":"1","author":"GZ Li","year":"2012","unstructured":"Li GZ, Yan SX, You M et al (2012) Intelligent ZHENG classification of hypertension depending on ML-kNN and information fusion. Evid-Based Complement Altern Med 1:837245","journal-title":"Evid-Based Complement Altern Med"},{"issue":"12","key":"2447_CR18","doi-asserted-by":"publisher","first-page":"9725","DOI":"10.1109\/TPAMI.2021.3125687","volume":"44","author":"X Li","year":"2021","unstructured":"Li X, Zhang H, Zhang R (2021) Adaptive graph auto-encoder for general data clustering. IEEE Trans Pattern Anal Mach Intell 44(12):9725\u20139732","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"2447_CR19","doi-asserted-by":"publisher","first-page":"97","DOI":"10.5335\/rbca.v12i3.11117","volume":"12","author":"SP Lima","year":"2020","unstructured":"Lima SP, Cruz MD (2020) A genetic algorithm using Calinski-Harabasz index for automatic clustering problem. Revista Brasileira de Computa\u00e7\u00e3o Aplicada 12(3):97\u2013106","journal-title":"Revista Brasileira de Computa\u00e7\u00e3o Aplicada"},{"key":"2447_CR20","doi-asserted-by":"crossref","unstructured":"Liu B, Guan W, Fang Z (2023) Research on TCM syndrome differentiation based on multi-feature fusion and GCN. In: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023). SPIE, pp 201\u2013209","DOI":"10.1117\/12.2682399"},{"key":"2447_CR21","unstructured":"Mucheng R, Heyan H, Yuxiang Z et al (2022) TCM-SD: a benchmark for probing syndrome differentiation via Natural Language processing. In: Proceedings of the 21st Chinese National Conference on Computational Linguistics, pp 908\u2013920"},{"key":"2447_CR22","doi-asserted-by":"crossref","unstructured":"Nam J, Kim J, Loza\u00a0Menc\u00eda E et\u00a0al (2014) Large-scale multi-label text classification\u2014revisiting neural networks. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15\u201319, 2014. Proceedings, Part II 14. Springer, pp 437\u2013452","DOI":"10.1007\/978-3-662-44851-9_28"},{"key":"2447_CR23","doi-asserted-by":"crossref","unstructured":"Peng H, Zhang J, Huang X et\u00a0al (2024) Unsupervised social bot detection via structural information theory. arXiv preprint arXiv:2404.13595","DOI":"10.1145\/3660522"},{"key":"2447_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107662","volume":"235","author":"T Pham","year":"2022","unstructured":"Pham T, Tao X, Zhang J et al (2022) Graph-based multi-label disease prediction model learning from medical data and domain knowledge. Knowl-Based Syst 235:107662","journal-title":"Knowl-Based Syst"},{"key":"2447_CR25","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read J, Pfahringer B, Holmes G et al (2011) Classifier chains for multi-label classification. Mach Learn 85:333\u2013359","journal-title":"Mach Learn"},{"key":"2447_CR26","first-page":"10","volume":"3","author":"AM Santos","year":"2011","unstructured":"Santos AM, Canuto AM, Neto AF (2011) A comparative analysis of classification methods to multi-label tasks in different application domains. Int J Comput Inf Syst Ind Manag Appl 3:10","journal-title":"Int J Comput Inf Syst Ind Manag Appl"},{"key":"2447_CR27","unstructured":"Sun Z, Deng ZH, Nie JY et\u00a0al (2019) Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197"},{"key":"2447_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eujim.2023.102288","volume":"62","author":"S Teng","year":"2023","unstructured":"Teng S, Fu A, Lu W et al (2023) TCM syndrome classification using graph convolutional network. Eur J Integr Med 62:102288","journal-title":"Eur J Integr Med"},{"issue":"4.19","key":"2447_CR29","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.14419\/ijet.v7i4.19.28284","volume":"7","author":"VS Tidake","year":"2018","unstructured":"Tidake VS, Sane SS (2018) Multi-label classification: a survey. Int J Eng Technol 7(4.19):1045\u20131054","journal-title":"Int J Eng Technol"},{"key":"2447_CR30","doi-asserted-by":"crossref","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2010) Mining multi-label data. In: Data mining and knowledge discovery handbook, pp 667\u2013685","DOI":"10.1007\/978-0-387-09823-4_34"},{"issue":"1","key":"2447_CR31","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten L (2014) Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 15(1):3221\u20133245","journal-title":"J Mach Learn Res"},{"issue":"11","key":"2447_CR32","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"2447_CR33","unstructured":"Vaswani A, Shazeer N, Parmar N et\u00a0al (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30"},{"key":"2447_CR34","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et\u00a0al (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"2447_CR35","doi-asserted-by":"crossref","unstructured":"Wang C, Pan S, Hu R et\u00a0al (2019) Attributed graph clustering: a deep attentional embedding approach. arXiv preprint arXiv:1906.06532","DOI":"10.24963\/ijcai.2019\/509"},{"key":"2447_CR36","doi-asserted-by":"crossref","unstructured":"Wang X, Xu Y (2019) An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index. In: IOP conference series: materials science and engineering. IOP Publishing, p 052024","DOI":"10.1088\/1757-899X\/569\/5\/052024"},{"key":"2447_CR37","doi-asserted-by":"crossref","unstructured":"Wang Y, Wu Q, Zhu J et\u00a0al (2020) A TCM syndrome differentiation algorithm based on deep belief network. In: 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, pp 166\u2013171","DOI":"10.1109\/YAC51587.2020.9337614"},{"key":"2447_CR38","doi-asserted-by":"crossref","unstructured":"Weng H, Liu Z, Maxwell A et\u00a0al (2018) Multi-label symptom analysis and modeling of TCM diagnosis of hypertension. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp 1922\u20131929","DOI":"10.1109\/BIBM.2018.8621173"},{"key":"2447_CR39","unstructured":"Wu B, Ding S, Xu X et\u00a0al (2024) Synergistic deep graph clustering network. arXiv preprint arXiv:2406.15797"},{"key":"2447_CR40","unstructured":"Wu XZ, Zhou ZH (2017) A unified view of multi-label performance measures. In: International conference on machine learning. PMLR, pp 3780\u20133788"},{"key":"2447_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120479","volume":"229","author":"X Yan","year":"2023","unstructured":"Yan X, Yu X, Hu S et al (2023) Mutual Boost Network for attributed graph clustering. Expert Syst Appl 229:120479","journal-title":"Expert Syst Appl"},{"key":"2447_CR42","doi-asserted-by":"crossref","unstructured":"Yang L, Wu XZ, Jiang Y et\u00a0al (2020) Multi-label learning with deep forest. In: ECAI 2020. IOS Press, p 1634\u20131641","DOI":"10.3233\/FAIA200274"},{"key":"2447_CR43","doi-asserted-by":"crossref","unstructured":"Yang M, Liu Z, Yang L et\u00a0al (2023) Group identification via transitional hypergraph convolution with cross-view self-supervised learning. In: Proceedings of the 32nd ACM international conference on information and knowledge management, pp 2969\u20132979","DOI":"10.1145\/3583780.3614902"},{"key":"2447_CR44","doi-asserted-by":"crossref","unstructured":"Yang M, Liu Z, Yang L et\u00a0al (2024) Unified pretraining for recommendation via task hypergraphs. In: Proceedings of the 17th ACM international conference on web search and data mining, pp 891\u2013900","DOI":"10.1145\/3616855.3635811"},{"key":"2447_CR45","doi-asserted-by":"publisher","first-page":"11412","DOI":"10.1007\/s10489-024-05546-3","volume":"54","author":"Q Yang","year":"2024","unstructured":"Yang Q, Cheng Z, Kang Y et al (2024) A novel multi-view contrastive learning for herb recommendation. Appl Intell 54:11412\u201311429","journal-title":"Appl Intell"},{"key":"2447_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2021.11.010","volume":"146","author":"Y Yang","year":"2022","unstructured":"Yang Y, Rao Y, Yu M et al (2022) Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation. Neural Netw 146:1\u201310","journal-title":"Neural Netw"},{"key":"2447_CR47","doi-asserted-by":"crossref","unstructured":"Zhang D, Gan Z, Huang Z (2019) Study on classification model of traditional Chinese medicine syndrome types of stroke patients in convalescent stage based on support vector machine. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME). IEEE, pp 205\u2013209","DOI":"10.1109\/ITME.2019.00053"},{"key":"2447_CR48","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.ijar.2018.10.009","volume":"103","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Miao D, Zhang Z et al (2018) A three-way selective ensemble model for multi-label classification. Int J Approx Reason 103:394\u2013413","journal-title":"Int J Approx Reason"},{"key":"2447_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.jep.2022.115109","volume":"297","author":"W Zhao","year":"2022","unstructured":"Zhao W, Lu W, Li Z et al (2022) Tcm herbal prescription recommendation model based on multi-graph convolutional network. J Ethnopharmacol 297:115109","journal-title":"J Ethnopharmacol"},{"issue":"3","key":"2447_CR50","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s13042-022-01671-y","volume":"14","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Song K, Ren X et al (2023) Attention Matching Network for few-shot learning in the syndrome differentiation of cerebral stroke. Int J Mach Learn Cybern 14(3):911\u2013927","journal-title":"Int J Mach Learn Cybern"},{"key":"2447_CR51","doi-asserted-by":"crossref","unstructured":"Zhou D, Huang J, Sch\u00f6lkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Advances in neural information processing systems, vol 19","DOI":"10.7551\/mitpress\/7503.003.0205"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02447-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02447-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02447-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:29:09Z","timestamp":1749270549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02447-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":51,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2447"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02447-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"5 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}