{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T23:49:36Z","timestamp":1781394576097,"version":"3.54.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04333-2","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T05:10:22Z","timestamp":1757394622000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing Clustering Performance: A Comparative Analysis of Metaheuristics and Enhanced Initialization Strategies"],"prefix":"10.1007","volume":"6","author":[{"given":"Duha","family":"Al-Darras","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nesreen A.","family":"Hamad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5214-6429","authenticated-orcid":false,"given":"Bashar","family":"Al-Shboul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"4333_CR1","doi-asserted-by":"publisher","unstructured":"Likas A, Vlassis N, J Verbeek J. The global k-means clustering algorithm. Pattern Recognition. 2003;36(2):451\u2013461 . https:\/\/doi.org\/10.1016\/S0031-3203(02)00060-2","DOI":"10.1016\/S0031-3203(02)00060-2"},{"key":"4333_CR2","doi-asserted-by":"publisher","unstructured":"Ratanavilisagul C. A novel modified particle swarm optimization algorithm with mutation for data clustering problem. 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), 2020:55\u201359 . https:\/\/doi.org\/10.1109\/ICCIA49625.2020.00018 . IEEE","DOI":"10.1109\/ICCIA49625.2020.00018"},{"issue":"7","key":"4333_CR3","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1080\/1206212X.2018.1521090","volume":"42","author":"W Xiaoqiong","year":"2020","unstructured":"Xiaoqiong W, Zhang YE. Image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering. Int J Comput Appl. 2020;42(7):649\u201354. https:\/\/doi.org\/10.1080\/1206212X.2018.1521090.","journal-title":"Int J Comput Appl"},{"issue":"1","key":"4333_CR4","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2346830","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA. Algorithm AS 136: a k-means clustering algorithm. Journal of the royal statistical society series c (applied statistics). 1979;28(1):100\u20138. https:\/\/doi.org\/10.2307\/2346830.","journal-title":"Journal of the royal statistical society. series c (applied statistics)"},{"key":"4333_CR5","unstructured":"Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SODA \u201907, pp. 1027\u20131035. Society for Industrial and Applied Mathematics, USA (2007)"},{"issue":"1","key":"4333_CR6","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1080\/00949658208810560","volume":"15","author":"WJ Welch","year":"1982","unstructured":"Welch WJ. Algorithmic complexity: three NP-hard problems in computational statistics. J Stat Comput Simul. 1982;15(1):17\u201325. https:\/\/doi.org\/10.1080\/00949658208810560.","journal-title":"J Stat Comput Simul"},{"issue":"2","key":"4333_CR7","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s12065-019-00212-x","volume":"12","author":"S Harifi","year":"2019","unstructured":"Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S. Emperor penguins colony: a new metaheuristic algorithm for optimization. Evol Intel. 2019;12(2):211\u201326. https:\/\/doi.org\/10.1007\/s12065-019-00212-x.","journal-title":"Evol Intel"},{"issue":"3","key":"4333_CR8","doi-asserted-by":"publisher","first-page":"297","DOI":"10.18280\/ria.340307","volume":"34","author":"S Harifi","year":"2020","unstructured":"Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S. Using metaheuristic algorithms to improve K-means clustering: a comparative study. Rev Intelligence Artif. 2020;34(3):297\u2013305. https:\/\/doi.org\/10.18280\/ria.340307.","journal-title":"Rev Intelligence Artif"},{"key":"4333_CR9","unstructured":"Shboul BA, Myaeng SH. Initializing k-means using genetic algorithms. International Conference on Computational Intelligence and Cognitive Informatics (ICCICI 09), pp. 114\u2013118 (2009)"},{"key":"4333_CR10","doi-asserted-by":"publisher","unstructured":"Al-Darras D, Hamad NA, Al-Shboul B. Exploring clustering improvement: A comparative study of utilizing metaheuristics and initialization strategies. Chbeir, R., Damiani, E., Dustdar, S., Manolopoulos, Y., Masciari, E., Pitoura, E., Rinaldi, A. (eds.) Management of Digital EcoSystems, pp. 296\u2013310. Springer, Cham (2026). https:\/\/doi.org\/10.1007\/978-3-031-93598-5_22","DOI":"10.1007\/978-3-031-93598-5_22"},{"key":"4333_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113702","volume":"161","author":"Q Askari","year":"2020","unstructured":"Askari Q, Saeed M, Younas I. Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl. 2020;161:113702. https:\/\/doi.org\/10.1016\/j.eswa.2020.113702.","journal-title":"Expert Syst Appl"},{"key":"4333_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.107050","volume":"152","author":"A Mohammadi-Balani","year":"2021","unstructured":"Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M. Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng. 2021;152:107050. https:\/\/doi.org\/10.1016\/j.cie.2020.107050.","journal-title":"Comput Ind Eng"},{"key":"4333_CR13","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw. 2017;114:163\u201391. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.07.002.","journal-title":"Adv Eng Softw"},{"key":"4333_CR14","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst. 2019;97:849\u201372. https:\/\/doi.org\/10.1016\/j.future.2019.02.028.","journal-title":"Futur Gener Comput Syst"},{"key":"4333_CR15","doi-asserted-by":"publisher","unstructured":"Merwe DW, Engelbrecht AP.: Data clustering using particle swarm optimization. The 2003 Congress on Evolutionary Computation, 2003. CEC\u201903., 2003;1:215\u2013220 . https:\/\/doi.org\/10.1109\/CEC.2003.1299577 . IEEE","DOI":"10.1109\/CEC.2003.1299577"},{"key":"4333_CR16","volume-title":"Combinatorial Optimization: Algorithms and Complexity","author":"CH Papadimitriou","year":"1998","unstructured":"Papadimitriou CH, Steiglitz K. Combinatorial Optimization: Algorithms and Complexity. US: Courier Corporation; 1998."},{"issue":"7","key":"4333_CR17","first-page":"12002","volume":"17","author":"AF Jahwar","year":"2020","unstructured":"Jahwar AF, Abdulazeez AM. Meta-heuristic algorithms for K-means clustering: a review. PalArch\u2019s Journal of Archaeology of Egypt\/Egyptology. 2020;17(7):12002\u201320.","journal-title":"PalArch\u2019s Journal of Archaeology of Egypt\/Egyptology"},{"issue":"1","key":"4333_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1(1):67\u201382. https:\/\/doi.org\/10.1109\/4235.585893.","journal-title":"IEEE Trans Evol Comput"},{"key":"4333_CR19","doi-asserted-by":"publisher","unstructured":"Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS\u201995. Proceedings of the Sixth International Symposium on Micro Machine And Human Science, 1995:39\u201343 . https:\/\/doi.org\/10.1109\/MHS.1995.494215 . IEEE","DOI":"10.1109\/MHS.1995.494215"},{"issue":"2","key":"4333_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/BF00175354","volume":"4","author":"D Whitley","year":"1994","unstructured":"Whitley D. A genetic algorithm tutorial. Stat Comput. 1994;4(2):65\u201385. https:\/\/doi.org\/10.1007\/BF00175354.","journal-title":"Stat Comput"},{"key":"4333_CR21","volume-title":"Differential Evolution","author":"V Feoktistov","year":"2006","unstructured":"Feoktistov V. Differential Evolution. US: Springer; 2006."},{"key":"4333_CR22","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim. 2007;39:459\u201371. https:\/\/doi.org\/10.1007\/s10898-007-9149-x.","journal-title":"J Glob Optim"},{"issue":"6","key":"4333_CR23","first-page":"90","volume":"1","author":"TM Kodinariya","year":"2013","unstructured":"Kodinariya TM, Makwana PR. Review on determining number of cluster in K-means clustering. Int J. 2013;1(6):90\u20135.","journal-title":"Int J"},{"key":"4333_CR24","unstructured":"UCI Machine Learning Repository (2021). https:\/\/archive.ics.uci.edu\/ Accessed 2024-11-10"},{"issue":"1","key":"4333_CR25","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s12065-020-00562-x","volume":"15","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar Y. A new metaheuristic algorithm based on water wave optimization for data clustering. Evol Intel. 2022;15(1):759\u201383. https:\/\/doi.org\/10.1007\/s12065-020-00562-x.","journal-title":"Evol Intel"},{"key":"4333_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-023-01019-2","author":"M Daviran","year":"2023","unstructured":"Daviran M, Ghezelbash R, Niknezhad M, Maghsoudi A, Ghaeminejad H. Hybridizing K-means clustering algorithm with harmony search and artificial bee colony optimizers for intelligence mineral prospectivity mapping. Earth Sci Inform. 2023. https:\/\/doi.org\/10.1007\/s12145-023-01019-2.","journal-title":"Earth Sci Inform"},{"key":"4333_CR27","doi-asserted-by":"publisher","unstructured":"Hua C, Wei W. A particle swarm optimization k-means algorithm for mongolian elements clustering. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019: 1559\u20131564 . https:\/\/doi.org\/10.1109\/SSCI44817.2019.9003077 . IEEE","DOI":"10.1109\/SSCI44817.2019.9003077"},{"issue":"1","key":"4333_CR28","doi-asserted-by":"publisher","first-page":"5434","DOI":"10.1038\/s41598-024-55619-z","volume":"14","author":"M Premkumar","year":"2024","unstructured":"Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, et al. Augmented weighted K-means grey wolf optimizer: an enhanced metaheuristic algorithm for data clustering problems. Sci Rep. 2024;14(1):5434. https:\/\/doi.org\/10.1038\/s41598-024-55619-z.","journal-title":"Sci Rep"},{"issue":"2","key":"4333_CR29","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s10660-021-09478-9","volume":"22","author":"R Rashidi","year":"2022","unstructured":"Rashidi R, Khamforoosh K, Sheikhahmadi A. Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems. Electron Commer Res. 2022;22(2):623\u201348. https:\/\/doi.org\/10.1007\/s10660-021-09478-9.","journal-title":"Electron Commer Res"},{"issue":"2","key":"4333_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10020101","volume":"10","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Gandomi AH, Elaziz MA, Hamad HA, Omari M, Alshinwan M, et al. Advances in meta-heuristic optimization algorithms in big data text clustering. Electronics. 2021;10(2):101. https:\/\/doi.org\/10.3390\/electronics10020101.","journal-title":"Electronics"},{"key":"4333_CR31","doi-asserted-by":"publisher","unstructured":"Binu\u00a0Jose A, Das P. A multi-objective approach for inter-cluster and intra-cluster distance analysis for numeric data. In: Soft Computing: Theories and Applications: Proceedings of SoCTA .2022;2021: 319\u2013332 . https:\/\/doi.org\/10.1007\/978-981-19-0707-4_30 . Springer","DOI":"10.1007\/978-981-19-0707-4_30"},{"key":"4333_CR32","doi-asserted-by":"publisher","unstructured":"Dudek A. Silhouette index as clustering evaluation tool. Classification and Data Analysis: Theory and Applications.2020; 28: 19\u201333. https:\/\/doi.org\/10.1007\/978-3-030-52348-0_2 . Springer","DOI":"10.1007\/978-3-030-52348-0_2"},{"key":"4333_CR33","unstructured":"Hammouda K, Karray F. A comparative study of data clustering techniques. University of Waterloo, Ontario, Canada .2000;1."},{"key":"4333_CR34","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","volume":"133","author":"R Zhu","year":"2020","unstructured":"Zhu R, Guo Y, Xue J-H. Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recogn Lett. 2020;133:217\u201323. https:\/\/doi.org\/10.1016\/j.patrec.2020.03.004.","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"4333_CR35","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B. Learning from imbalanced data: open challenges and future directions. Prog Artif Intell. 2016;5(4):221\u201332. https:\/\/doi.org\/10.1007\/s13748-016-0094-0.","journal-title":"Prog Artif Intell"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04333-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04333-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04333-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T06:48:35Z","timestamp":1757486915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04333-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["4333"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04333-2","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]},"assertion":[{"value":"17 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","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"}}],"article-number":"810"}}