{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:58:50Z","timestamp":1743141530978,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031201011"},{"type":"electronic","value":"9783031201028"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-20102-8_30","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"385-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Medical Data Clustering Based on Multi-objective Clustering Algorithm"],"prefix":"10.1007","author":[{"given":"Shilian","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingsi","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junkai","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"30_CR1","unstructured":"Office of the State Council: Guiding Opinions of the General Office of the State Council on Promoting and Regulating the Development of Health Medical Person Data Application (2021)"},{"issue":"1","key":"30_CR2","first-page":"5","volume":"43","author":"P Ji","year":"2022","unstructured":"Ji, P., Zhu, D., Xie, Y.X.: Reflections on the application of scientific research sharing of health and medical data. Medicine and Philosophy 43(1), 5\u20138 (2022)","journal-title":"Medicine and Philosophy"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Andreopoulos, B., An, A., Wang, X., Schroeder, M.: A roadmap of clustering algorithms: Finding a match for a biomedical application. Briefings in Bioinformatics 10(3), 297\u2013314 (2009)","DOI":"10.1093\/bib\/bbn058"},{"key":"30_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72950-1_77","volume-title":"Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems","author":"D Karaboga","year":"2007","unstructured":"Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Springer, International fuzzy systems association world congress (2007)"},{"key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103307","volume":"87","author":"E Hancer","year":"2020","unstructured":"Hancer, E.: A new multi-objective differential evolution approach for simultaneous clustering and feature selection. Eng. Appl. Artif. Intell. 87, 103307 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"15","key":"30_CR6","doi-asserted-by":"publisher","first-page":"11545","DOI":"10.1007\/s00500-019-04620-0","volume":"24","author":"RJ Kuo","year":"2020","unstructured":"Kuo, R.J., Zulvia, F.E.: Multi-objective cluster analysis using a gradient evolution algorithm. Soft. Comput. 24(15), 11545\u201311559 (2020)","journal-title":"Soft. Comput."},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.eswa.2019.06.056","volume":"137","author":"D Dutta","year":"2019","unstructured":"Dutta, D., Sil, J., Dutta, P.: Automatic clustering by multi-objective genetic algorithm with numeric and categorical features. Expert Syst. Appl. 137, 357\u2013379 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"30_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/BF01890115","volume":"1","author":"WHE Day","year":"1984","unstructured":"Day, W.H.E., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1(1), 7\u201324 (1984)","journal-title":"J. Classif."},{"key":"30_CR9","unstructured":"MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA (1967)"},{"issue":"2","key":"30_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Rec. 25(2), 103\u2013114 (1996)","journal-title":"ACM SIGMOD Rec."},{"key":"30_CR11","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-0-387-73003-5_196","volume":"41","author":"DA Reynolds","year":"2009","unstructured":"Reynolds, D.A.: Gaussian mixture model. Encyclopedia of biometrics 41, 659\u2013663 (2009)","journal-title":"Encyclopedia of biometrics"},{"key":"30_CR12","volume-title":"Algorithms for Clustering Data","author":"AK Jain","year":"1988","unstructured":"Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc, Upper Saddle River, NJ, USA (1988)"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Rai, P., Singh, S.: A survey of clustering techniques. International Journal of Computer Applications 7(12), (2010)","DOI":"10.5120\/1326-1808"},{"issue":"6","key":"30_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0129126","volume":"10","author":"C Higuera","year":"2015","unstructured":"Higuera, C., Gardiner, K.J., Cios, K.J.: Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome. PLoS ONE 10(6), e0129126 (2015)","journal-title":"PLoS ONE"},{"issue":"6","key":"30_CR15","doi-asserted-by":"publisher","first-page":"10097","DOI":"10.1016\/j.eswa.2009.01.012","volume":"36","author":"R Majhi","year":"2009","unstructured":"Majhi, R., Panda, G., Majhi, B., Sahoo, G.: Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Syst. Appl. 36(6), 10097\u201310104 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"30_CR16","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1016\/j.eswa.2008.01.061","volume":"36","author":"L Zhao","year":"2009","unstructured":"Zhao, L., Yang, Y.: PSO-based single multiplicative neuron model for time series prediction. Expert Syst. Appl. 36(2), 2805\u20132812 (2009)","journal-title":"Expert Syst. Appl."},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Kang, H.I.: A fuzzy time series prediction method using the evolutionary algorithm. In International Conference on Intelligent Computing. 530\u2013537. Springer, Berlin, Heidelberg (2005)","DOI":"10.1007\/11538356_55"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20102-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:32:28Z","timestamp":1673537548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20102-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031201011","9783031201028"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20102-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}