{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:44:20Z","timestamp":1775565860464,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-026-08375-y","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:46:01Z","timestamp":1775562361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Empowering clustering: a synergy of consensus-based genetic and K-means algorithms"],"prefix":"10.1007","volume":"82","author":[{"given":"Sadegh","family":"Rezaei","sequence":"first","affiliation":[]},{"given":"Razieh","family":"Malekhosseini","sequence":"additional","affiliation":[]},{"given":"S. Hadi","family":"Yaghoubyan","sequence":"additional","affiliation":[]},{"given":"Karamollah","family":"Bagherifard","sequence":"additional","affiliation":[]},{"given":"Samad","family":"Nejatian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"8375_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121557","volume":"237","author":"J Xu","year":"2024","unstructured":"Xu J, Li T, Zhang D, Wu J (2024) Ensemble clustering via fusing global and local structure information. Expert Syst Appl 237:121557","journal-title":"Expert Syst Appl"},{"issue":"1","key":"8375_CR2","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/01969722.2022.2110682","volume":"55","author":"B Zhou","year":"2024","unstructured":"Zhou B, Lu B, Saeidlou S (2024) A hybrid clustering method based on the several diverse basic clustering and meta-clustering aggregation technique. Cybernet Syst 55(1):203\u2013229","journal-title":"Cybernet Syst"},{"issue":"5","key":"8375_CR3","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1080\/01969722.2022.2073704","volume":"54","author":"W Li","year":"2023","unstructured":"Li W, Wang Z, Sun W, Bahrami S (2023) An ensemble clustering framework based on hierarchical clustering ensemble selection and clusters clustering. Cybernet Syst 54(5):741\u2013766","journal-title":"Cybernet Syst"},{"issue":"2","key":"8375_CR4","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s13042-022-01651-2","volume":"14","author":"D Zhang","year":"2023","unstructured":"Zhang D, Yang Y, Qiu H (2023) Two-stage semi-supervised clustering ensemble framework based on constraint weight. Int J Mach Learn Cybernet 14(2):567\u2013586","journal-title":"Int J Mach Learn Cybernet"},{"issue":"8","key":"8375_CR5","doi-asserted-by":"publisher","first-page":"8219","DOI":"10.1007\/s10462-022-10366-3","volume":"56","author":"X Ran","year":"2023","unstructured":"Ran X, Xi Y, Lu Y, Wang X, Lu Z (2023) Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artif Intell Rev 56(8):8219\u20138264","journal-title":"Artif Intell Rev"},{"key":"8375_CR6","doi-asserted-by":"publisher","first-page":"7076","DOI":"10.1109\/ACCESS.2021.3049157","volume":"9","author":"X Li","year":"2022","unstructured":"Li X, Zhang Y, Cheng H, Zhou F, Yin B (2022) An unsupervised ensemble clustering approach for the analysis of student behavioral patterns. IEEE Access 9:7076\u20137091","journal-title":"IEEE Access"},{"issue":"1","key":"8375_CR7","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1016\/j.jksuci.2019.09.013","volume":"34","author":"S Khedairia","year":"2022","unstructured":"Khedairia S, Khadir MT (2022) A multiple clustering combination approach based on an iterative voting process. J King Saud Univers Comput Inf Sci 34(1):1370\u20131380","journal-title":"J King Saud Univers Comput Inf Sci"},{"key":"8375_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2019.101754","volume":"124","author":"SS Hamidi","year":"2019","unstructured":"Hamidi SS, Akbari E, Motameni H (2019) Consensus clustering algorithm based on the automatic partitioning likeness graph. Data Knowl Eng 124:101754","journal-title":"Data Knowl Eng"},{"issue":"2","key":"8375_CR9","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s10462-018-9642-2","volume":"52","author":"SO Abbasi","year":"2019","unstructured":"Abbasi SO, Nejatian S, Parvin H, Rezaie V, Bagherifard K (2019) Clustering ensemble selection considering quality and diversity. Artif Intell Rev 52(2):1311\u20131340","journal-title":"Artif Intell Rev"},{"issue":"8","key":"8375_CR10","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1016\/j.apr.2020.05.005","volume":"11","author":"T Stolz","year":"2020","unstructured":"Stolz T, Huertas ME, Mendoza A (2020) Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico. Atmos Pollut Res 11(8):1271\u20131280","journal-title":"Atmos Pollut Res"},{"key":"8375_CR11","doi-asserted-by":"crossref","unstructured":"Sarkar JP, Saha I, Maulik U (2019) Improved fuzzy clustering using ensemble-based differential evolution for remote sensing images. In: TENCON 2019\u20132019 IEEE Region 10 Conference (TENCON). IEEE, pp 880\u2013885, Oct 2019","DOI":"10.1109\/TENCON.2019.8929675"},{"key":"8375_CR12","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J (2002) Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583\u2013617","journal-title":"J Mach Learn Res"},{"issue":"6","key":"8375_CR13","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1109\/TPAMI.2005.113","volume":"27","author":"AL Fred","year":"2005","unstructured":"Fred AL, Jain AK (2005) Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Mach Intell 27(6):835\u2013850","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8375_CR14","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.patcog.2015.08.015","volume":"50","author":"D Huang","year":"2016","unstructured":"Huang D, Lai J, Wang CD (2016) Ensemble clustering using a factor graph. Pattern Recognit 50:131\u2013142","journal-title":"Pattern Recognit"},{"issue":"12","key":"8375_CR15","doi-asserted-by":"publisher","first-page":"2396","DOI":"10.1109\/TPAMI.2011.84","volume":"33","author":"N Iam-On","year":"2011","unstructured":"Iam-On N, Boongoen T, Garrett S, Price C (2011) A link-based approach to the cluster ensemble problem. IEEE Trans Pattern Anal Mach Intell 33(12):2396\u20132409","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"8375_CR16","doi-asserted-by":"publisher","first-page":"10301","DOI":"10.1007\/s11227-023-05822-y","volume":"80","author":"RC Sahoo","year":"2024","unstructured":"Sahoo RC, Kumar T, Tanwar P, Pruthi J, Singh S (2024) An efficient meta-heuristic algorithm based on water flow optimizer for data clustering. J Supercomput 80(8):10301\u201310326","journal-title":"J Supercomput"},{"key":"8375_CR17","doi-asserted-by":"publisher","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga KP, Yang MS (2020) Unsupervised K-means clustering algorithm. IEEE Access 8:80716\u201380727","journal-title":"IEEE Access"},{"key":"8375_CR18","doi-asserted-by":"crossref","unstructured":"Shen X, Diamond S, Gu Y, Boyd S (2016) Disciplined convex-concave programming. In: 2016 IEEE 55th conference on decision and control (CDC). IEEE, Dec 2016, pp 1009\u20131014","DOI":"10.1109\/CDC.2016.7798400"},{"key":"8375_CR19","doi-asserted-by":"crossref","unstructured":"Yi J, Yang T, Jin R, Jain AK, Mahdavi M (2012) Robust ensemble clustering by matrix completion. In 2012 IEEE 12th international conference on data mining. IEEE, 2012 Dec, pp 1176\u20131181","DOI":"10.1109\/ICDM.2012.123"},{"key":"8375_CR20","doi-asserted-by":"crossref","unstructured":"Divekar A, Parekh M, Savla V, Mishra R, Shirole M (2018) Benchmarking datasets for anomaly-based network intrusion detection: KDD CUP 99 alternatives. In: 2018 IEEE 3rd international conference on computing, communication, and security (ICCCS). IEEE, 2018, Oct, pp 1\u20138","DOI":"10.1109\/CCCS.2018.8586840"},{"key":"8375_CR21","doi-asserted-by":"publisher","unstructured":"Janosi A, Steinbrunn W, Pfisterer M, Detrano R (1988) Heart disease. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C52P4X","DOI":"10.24432\/C52P4X"},{"key":"8375_CR22","doi-asserted-by":"publisher","unstructured":"Fisher RA (1988) Iris. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C56C76","DOI":"10.24432\/C56C76"},{"key":"8375_CR23","doi-asserted-by":"publisher","unstructured":"Aeberhard S, Forina M (1991) Wine. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5PC7J","DOI":"10.24432\/C5PC7J"},{"key":"8375_CR24","doi-asserted-by":"publisher","unstructured":"German B (1987) Glass identification. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5WW2P","DOI":"10.24432\/C5WW2P"},{"key":"8375_CR25","doi-asserted-by":"publisher","unstructured":"Wolberg W, Mangasarian O, Street N, Street W (1995) Breast cancer wisconsin (diagnostic). UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5DW2B","DOI":"10.24432\/C5DW2B"},{"key":"8375_CR26","doi-asserted-by":"publisher","unstructured":"Michalski RS, Chilausky RL (1988) Soybean (large). UCI machine learning repository. https:\/\/doi.org\/10.24432\/C5JG6Z.","DOI":"10.24432\/C5JG6Z"},{"key":"8375_CR27","doi-asserted-by":"publisher","unstructured":"Sigillito V, Wing S, Hutton L, Baker K (1989) Ionosphere. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5W01B.","DOI":"10.24432\/C5W01B"},{"key":"8375_CR28","doi-asserted-by":"publisher","unstructured":"Li F-F, Andreeto M, Ranzato M, Perona P (2022) Caltech 101 (1.0). CaltechDATA. https:\/\/doi.org\/10.22002\/D1.20086","DOI":"10.22002\/D1.20086"},{"key":"8375_CR29","doi-asserted-by":"publisher","unstructured":"Nakai K (1996) Ecoli. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5388M.","DOI":"10.24432\/C5388M"},{"key":"8375_CR30","doi-asserted-by":"publisher","unstructured":"Srinivasan A (1993) Statlog (Landsat Satellite). UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C55887","DOI":"10.24432\/C55887"},{"key":"8375_CR31","doi-asserted-by":"publisher","unstructured":"Blackard J (1998) Covertype. UCI machine learning repository. https:\/\/doi.org\/10.24432\/C50K5N.","DOI":"10.24432\/C50K5N"},{"issue":"12","key":"8375_CR32","doi-asserted-by":"publisher","first-page":"4743","DOI":"10.1007\/s10489-018-1238-7","volume":"48","author":"P F\u00e4nti","year":"2018","unstructured":"F\u00e4nti P, Sieranoja S (2018) K-means properties on six clustering benchmark datasets. Appl Intell 48(12):4743\u20134759. https:\/\/doi.org\/10.1007\/s10489-018-1238-7","journal-title":"Appl Intell"},{"key":"8375_CR33","doi-asserted-by":"publisher","unstructured":"Cole R, Fanty M (1991) ISOLET. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C51G69","DOI":"10.24432\/C51G69"},{"key":"8375_CR34","doi-asserted-by":"publisher","unstructured":"Buscema M, Terzi S, Tastle W (2010) Steel plates faults. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5J88N","DOI":"10.24432\/C5J88N"},{"issue":"5","key":"8375_CR35","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8375_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.015","volume":"187","author":"TY Tan","year":"2020","unstructured":"Tan TY, Zhang L, Lim CP (2020) Adaptive melanoma diagnosis using evolving clustering, ensemble, and deep neural networks. Knowl Based Syst 187:104807","journal-title":"Knowl Based Syst"},{"key":"8375_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105787","volume":"31","author":"V Zarikas","year":"2020","unstructured":"Zarikas V, Poulopoulos SG, Gareiou Z, Zervas E (2020) Clustering analysis of countries using the COVID-19 cases dataset. Data Brief 31:105787","journal-title":"Data Brief"},{"issue":"03","key":"8375_CR38","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1142\/S0218001411008683","volume":"25","author":"S Vega-Pons","year":"2011","unstructured":"Vega-Pons S, Ruiz-Shulcloper J (2011) A survey of clustering ensemble algorithms. Int J Pattern Recognit Artif Intell 25(03):337\u2013372","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"11","key":"8375_CR39","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2006.226","volume":"28","author":"LI Kuncheva","year":"2006","unstructured":"Kuncheva LI, Vetrov DP (2006) Evaluation of the stability of k-means clusters ensembles to the random initialization. IEEE Trans Pattern Anal Mach Intell 28(11):1798\u20131808","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"8375_CR40","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TGRS.2008.918647","volume":"46","author":"X Zhang","year":"2008","unstructured":"Zhang X, Jiao L, Liu F, Bo L, Gong M (2008) Spectral clustering ensemble applied to SAR image segmentation. IEEE Trans Geosci Remote Sens 46(7):2126\u20132136","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8375_CR41","doi-asserted-by":"crossref","unstructured":"Law MH, Topchy AP, Jain AK (2004) Multiobjective data clustering. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004. IEEE. vol 2, pp II\u2013II, June, 2004","DOI":"10.1109\/CVPR.2004.1315194"},{"issue":"3","key":"8375_CR42","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TCBB.2013.59","volume":"10","author":"Z Yu","year":"2013","unstructured":"Yu Z, Chen H, You J, Han G, Li L (2013) Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data. IEEE ACM Trans Comput Biol Bioinform 10(3):657\u2013670","journal-title":"IEEE ACM Trans Comput Biol Bioinform"},{"key":"8375_CR43","doi-asserted-by":"crossref","unstructured":"Nguyen N, Caruana R (2007) Consensus clusterings. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE, Oct 2007, pp 607\u2013612","DOI":"10.1109\/ICDM.2007.73"},{"issue":"3","key":"8375_CR44","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1023\/A:1009769707641","volume":"2","author":"Z Huang","year":"1998","unstructured":"Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov 2(3):283\u2013304","journal-title":"Data Min Knowl Discov"},{"issue":"4","key":"8375_CR45","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1109\/TNNLS.2020.2984814","volume":"32","author":"P Zhou","year":"2020","unstructured":"Zhou P, Du L, Liu X, Shen YD, Fan M, Li X (2020) Self-paced clustering ensemble. IEEE Trans Neural Netw Learn Syst 32(4):1497\u20131511","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08375-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08375-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08375-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:46:10Z","timestamp":1775562370000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08375-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,7]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["8375"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08375-y","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,7]]},"assertion":[{"value":"7 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"325"}}