{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:59:48Z","timestamp":1781153988571,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"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":[[2026,7]]},"DOI":"10.1007\/s13042-026-03085-6","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:33:59Z","timestamp":1781152439000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimized K-medoid: a comprehensive approach to medoid discovery and finding optimal K"],"prefix":"10.1007","volume":"17","author":[{"given":"Mahnoor","family":"Chaudhry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imran","family":"Shafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Mahnoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arslan","family":"Shaukat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiz Muhammad","family":"Raza ur Rehman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"3085_CR1","doi-asserted-by":"publisher","first-page":"114060","DOI":"10.1016\/j.eswa.2020.114060","volume":"166","author":"A Dogan","year":"2021","unstructured":"Dogan A, Birant D (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166:114060","journal-title":"Expert Syst Appl"},{"key":"3085_CR2","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.sbspro.2015.01.402","volume":"172","author":"NA Shukor","year":"2015","unstructured":"Shukor NA, Tasir Z, Meijden H (2015) An examination of online learning effectiveness using data mining. Procedia Soc Behav Sci 172:555\u2013562","journal-title":"Procedia Soc Behav Sci"},{"issue":"1","key":"3085_CR3","doi-asserted-by":"publisher","first-page":"103121","DOI":"10.1016\/j.ipm.2022.103121","volume":"60","author":"Y Liu","year":"2023","unstructured":"Liu Y, Alzahrani IR, Jaleel RA, Al Sulaie S (2023) An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis. Inform Process Manage 60(1):103121","journal-title":"Inform Process Manage"},{"key":"3085_CR4","doi-asserted-by":"crossref","unstructured":"Ghongade TG, Khobragade R (2023) Evaluation on utilization and emaciation of data mining techniques in information system. In: 2022 OPJU international technology conference on emerging technologies for sustainable development (OTCON), pp. 1\u20136 . IEEE","DOI":"10.1109\/OTCON56053.2023.10114009"},{"issue":"7","key":"3085_CR5","doi-asserted-by":"publisher","first-page":"6439","DOI":"10.1007\/s10462-022-10325-y","volume":"56","author":"GJ Oyewole","year":"2023","unstructured":"Oyewole GJ, Thopil GA (2023) Data clustering: application and trends. Artif Intell Rev 56(7):6439\u20136475","journal-title":"Artif Intell Rev"},{"key":"3085_CR6","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.ins.2020.08.121","volume":"545","author":"AV Ushakov","year":"2021","unstructured":"Ushakov AV, Vasilyev I (2021) Near-optimal large-scale k-medoids clustering. Inf Sci 545:344\u2013362","journal-title":"Inf Sci"},{"key":"3085_CR7","doi-asserted-by":"publisher","first-page":"109628","DOI":"10.1016\/j.rser.2019.109628","volume":"120","author":"A Rajabi","year":"2020","unstructured":"Rajabi A, Eskandari M, Ghadi MJ, Li L, Zhang J, Siano P (2020) A comparative study of clustering techniques for electrical load pattern segmentation. Renew Sustain Energy Rev 120:109628","journal-title":"Renew Sustain Energy Rev"},{"issue":"14","key":"3085_CR8","doi-asserted-by":"publisher","first-page":"8236","DOI":"10.3390\/su14148236","volume":"14","author":"M-K Chen","year":"2022","unstructured":"Chen M-K, Wu S-W, Huang Y-P, Chang F-J (2022) The key success factors for the operation of sme cluster business ecosystem. Sustainability 14(14):8236","journal-title":"Sustainability"},{"issue":"11","key":"3085_CR9","doi-asserted-by":"publisher","first-page":"15011","DOI":"10.1007\/s12652-019-01445-5","volume":"14","author":"T-HT Nguyen","year":"2023","unstructured":"Nguyen T-HT, Dinh D-T, Sriboonchitta S, Huynh V-N (2023) A method for k-means-like clustering of categorical data. J Ambient Intell Humaniz Comput 14(11):15011\u201315021","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"3085_CR10","doi-asserted-by":"crossref","unstructured":"Miraftabzadeh SM, Colombo CG, Longo M, Foiadelli F (2023) K-means and alternative clustering methods in modern power systems. IEEE Access","DOI":"10.1109\/ACCESS.2023.3327640"},{"issue":"4","key":"3085_CR11","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s11365-023-00882-1","volume":"19","author":"L S\u00e1ez-Ortu\u00f1o","year":"2023","unstructured":"S\u00e1ez-Ortu\u00f1o L, Huertas-Garcia R, Forgas-Coll S, Puertas-Prats E (2023) How can entrepreneurs improve digital market segmentation? a comparative analysis of supervised and unsupervised learning algorithms. Int Entrep Manag J 19(4):1893\u20131920","journal-title":"Int Entrep Manag J"},{"key":"3085_CR12","doi-asserted-by":"crossref","unstructured":"Lin K, Li X, Zhang Z, Chen J (2014) A k-means clustering with optimized initial center based on hadoop platform. In: 2014 9th International conference on computer science & education, pp. 263\u2013266. IEEE","DOI":"10.1109\/ICCSE.2014.6926466"},{"issue":"1","key":"3085_CR13","first-page":"5521119","volume":"2021","author":"G Du","year":"2021","unstructured":"Du G, Li X, Zhang L, Liu L, Zhao C (2021) Novel automated k-means++ algorithm for financial data sets. Math Probl Eng 2021(1):5521119","journal-title":"Math Probl Eng"},{"issue":"1","key":"3085_CR14","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/a14010006","volume":"14","author":"J H\u00e4m\u00e4l\u00e4inen","year":"2020","unstructured":"H\u00e4m\u00e4l\u00e4inen J, K\u00e4rkk\u00e4inen T, Rossi T (2020) Improving scalable k-means++. Algorithms 14(1):6","journal-title":"Algorithms"},{"issue":"5","key":"3085_CR15","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TEVC.2022.3144134","volume":"26","author":"C Baldassi","year":"2022","unstructured":"Baldassi C (2022) Recombinator-k-means: an evolutionary algorithm that exploits k-means++ for recombination. IEEE Trans Evol Comput 26(5):991\u20131003","journal-title":"IEEE Trans Evol Comput"},{"key":"3085_CR16","doi-asserted-by":"crossref","unstructured":"Min Z, Kai-fei D (2015) Improved research to k-means initial cluster centers. In: 2015 Ninth international conference on frontier of computer science and technology, pp. 349\u2013353. IEEE","DOI":"10.1109\/FCST.2015.61"},{"issue":"7","key":"3085_CR17","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1016\/j.patcog.2014.01.015","volume":"47","author":"G Tzortzis","year":"2014","unstructured":"Tzortzis G, Likas A (2014) The minmax k-means clustering algorithm. Pattern Recogn 47(7):2505\u20132516","journal-title":"Pattern Recogn"},{"issue":"2","key":"3085_CR18","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s12530-022-09447-z","volume":"14","author":"KK Pandey","year":"2023","unstructured":"Pandey KK, Shukla D (2023) Min max kurtosis distance based improved initial centroid selection approach of k-means clustering for big data mining on gene expression data. Evol Syst 14(2):207\u2013244","journal-title":"Evol Syst"},{"key":"3085_CR19","doi-asserted-by":"crossref","unstructured":"Mehta S, Singh M, Pamula R (2020) Analysis of min-max algorithm and pso algorithm for data clustering. In: Proceedings of the 2020 International conference on big data in management, pp. 23\u201327","DOI":"10.1145\/3437075.3437076"},{"issue":"6","key":"3085_CR20","first-page":"1943","volume":"5","author":"J Swarndeep Saket","year":"2016","unstructured":"Swarndeep Saket J, Pandya S (2016) An overview of partitioning algorithms in clustering techniques. Int J Adv Res Comput Eng Technol (IJARCET) 5(6):1943\u20131946","journal-title":"Int J Adv Res Comput Eng Technol (IJARCET)"},{"issue":"1","key":"3085_CR21","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/JSYST.2021.3056547","volume":"16","author":"S Zhang","year":"2021","unstructured":"Zhang S, Wang Y, Zhang Y, Wan P, Zhuang J (2021) Riemannian distance-based fast k-medoids clustering algorithm for cooperative spectrum sensing. IEEE Syst J 16(1):880\u2013890","journal-title":"IEEE Syst J"},{"key":"3085_CR22","doi-asserted-by":"crossref","unstructured":"Madhulatha TS (2011) Comparison between k-means and k-medoids clustering algorithms. In: International conference on advances in computing and information technology, pp. 472\u2013481. Springer","DOI":"10.1007\/978-3-642-22555-0_48"},{"key":"3085_CR23","unstructured":"Surya P, Laurence Aroquiaraj I (2019) Performance analysis of k-means and k-medoid clustering algorithms using agriculture dataset. J Emerg Technol Innov Res (JETIR) 6(1)"},{"issue":"1","key":"3085_CR24","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1186\/s12859-023-05569-6","volume":"24","author":"J Domingo","year":"2023","unstructured":"Domingo J, Kutsyr-Kolesnyk O, Leon T, Perez-Moraga R, Ayala G, Roson B (2023) A cell abundance analysis based on efficient pam clustering for a better understanding of the dynamics of endometrial remodelling. BMC Bioinform 24(1):440","journal-title":"BMC Bioinform"},{"issue":"4","key":"3085_CR25","first-page":"227","volume":"2","author":"L Sun","year":"2017","unstructured":"Sun L, Chen G, Xiong H, Guo C (2017) Cluster analysis in data-driven management and decisions. J Manage Sci Eng 2(4):227\u2013251","journal-title":"J Manage Sci Eng"},{"key":"3085_CR26","doi-asserted-by":"publisher","first-page":"101804","DOI":"10.1016\/j.is.2021.101804","volume":"101","author":"E Schubert","year":"2021","unstructured":"Schubert E, Rousseeuw PJ (2021) Fast and eager k-medoids clustering: O (k) runtime improvement of the pam, clara, and clarans algorithms. Inf Syst 101:101804","journal-title":"Inf Syst"},{"key":"3085_CR27","doi-asserted-by":"crossref","unstructured":"Song H, Lee J-G, Han W-S (2017) Pamae: parallel k-medoids clustering with high accuracy and efficiency. In: Proceedings of the 23rd ACM SIGKDD international Conference on knowledge discovery and data mining, pp. 1087\u20131096","DOI":"10.1145\/3097983.3098098"},{"key":"3085_CR28","doi-asserted-by":"crossref","unstructured":"Brankovic M, Buchin K, Klaren K, Nusser A, Popov A, Wong S (2020) (k, l)-medians clustering of trajectories using continuous dynamic time warping. In: Proceedings of the 28th international conference on advances in geographic information systems, pp. 99\u2013110","DOI":"10.1145\/3397536.3422245"},{"key":"3085_CR29","doi-asserted-by":"crossref","unstructured":"Mathelin A, Cecchi NE, Deheeger F, Mougeot M, Vayatis N (2025) Onebatchpam: a fast and frugal k-medoids algorithm. In: Proceedings of the AAAI conference on artificial intelligence, vol. 39, pp. 16172\u201316180","DOI":"10.1609\/aaai.v39i15.33776"},{"key":"3085_CR30","doi-asserted-by":"crossref","unstructured":"Gupta T, Panda SP (2019) Clustering validation of clara and k-means using silhouette & dunn measures on iris dataset. In: 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), pp. 10\u201313. IEEE","DOI":"10.1109\/COMITCon.2019.8862199"},{"key":"3085_CR31","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.matpr.2020.01.110","volume":"27","author":"S Renjith","year":"2020","unstructured":"Renjith S, Sreekumar A, Jathavedan M (2020) Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets. Mater Today Proc 27:627\u2013633","journal-title":"Mater Today Proc"},{"issue":"3","key":"3085_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3490384","volume":"55","author":"SK Anand","year":"2022","unstructured":"Anand SK, Kumar S (2022) Experimental comparisons of clustering approaches for data representation. ACM Comput Surv (CSUR) 55(3):1\u201333","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"5","key":"3085_CR33","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","volume":"14","author":"RT Ng","year":"2002","unstructured":"Ng RT, Han J (2002) Clarans: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003\u20131016","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"3085_CR34","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/s42979-023-01906-x","volume":"4","author":"N Goel","year":"2023","unstructured":"Goel N, Singaravelu M, Gupta S, Namana S, Singh R, Kumar R (2023) Parameterized clustering cleaning approach for high-dimensional datasets with class overlap and imbalance. SN Comput Sci 4(5):464","journal-title":"SN Comput Sci"},{"key":"3085_CR35","doi-asserted-by":"crossref","unstructured":"Sarang P (2023) Clarans: clustering large datasets with randomized search. In: Thinking data science: a data science practitioner\u2019s guide, pp. 237\u2013242. Springer","DOI":"10.1007\/978-3-031-02363-7_14"},{"key":"3085_CR36","doi-asserted-by":"crossref","unstructured":"Lenssen L, Schubert E (2022) Clustering by direct optimization of the medoid silhouette. In: International conference on similarity search and applications, pp. 190\u2013204. Springer","DOI":"10.1007\/978-3-031-17849-8_15"},{"key":"3085_CR37","doi-asserted-by":"crossref","unstructured":"Zhang J, Wang H (2023) Analysis of clarans algorithm for weather data based on spark. Computers Materials & Continua 76(2)","DOI":"10.32604\/cmc.2023.038462"},{"issue":"7","key":"3085_CR38","doi-asserted-by":"publisher","first-page":"349","DOI":"10.3390\/a16070349","volume":"16","author":"L Nigro","year":"2023","unstructured":"Nigro L, Fr\u00e4nti P (2023) Two medoid-based algorithms for clustering sets. Algorithms 16(7):349","journal-title":"Algorithms"},{"issue":"3\u20134","key":"3085_CR39","first-page":"137","volume":"13","author":"H Drias","year":"2016","unstructured":"Drias H, Kechid A, Fodil-Cherif N (2016) A hybrid clustering algorithm and web information foraging. Int J Hybrid Intell Syst 13(3\u20134):137\u2013149","journal-title":"Int J Hybrid Intell Syst"},{"key":"3085_CR40","unstructured":"Aswad NA. Diamonds prices. https:\/\/www.kaggle.com\/datasets\/nancyalaswad90\/diamonds-prices"},{"key":"3085_CR41","unstructured":"Nkongolo MW. UGRansome dataset. https:\/\/www.kaggle.com\/datasets\/nkongolo\/ugransome-dataset"},{"key":"3085_CR42","unstructured":"Fahmida: customer segmentation data for marketing analysis. https:\/\/www.kaggle.com\/datasets\/fahmidachowdhury\/customer-segmentation-data-for-marketing-analysis"},{"key":"3085_CR43","unstructured":"Ofer D. COMPAS recidivism racial bias. https:\/\/www.kaggle.com\/datasets\/danofer\/compass"},{"key":"3085_CR44","unstructured":"Kaggle: Spotify Data:Popular Hip-Hop Artists and Tracks. https:\/\/www.kaggle.com\/datasets\/kanchana1990\/spotify-datapopular-hip-hop-artists-and-tracks"},{"issue":"7","key":"3085_CR45","first-page":"2972","volume":"4","author":"J Yadav","year":"2013","unstructured":"Yadav J, Sharma M (2013) A review of k-mean algorithm. Int J Eng Trends Technol 4(7):2972\u20132976","journal-title":"Int J Eng Trends Technol"},{"key":"3085_CR46","doi-asserted-by":"crossref","unstructured":"Zeng Y, Wang M, Fan B (2021) Cooperative charging algorithm based on k-mean++ clustering for wrsn. In: 2021 IEEE Intl conf on parallel & distributed processing with applications, big data & cloud computing, sustainable computing & communications, social computing & networking (ISPA\/BDCloud\/SocialCom\/SustainCom), pp. 738\u2013744. IEEE","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00106"},{"key":"3085_CR47","doi-asserted-by":"publisher","unstructured":"Jin X, Han J (2010) K-medoids clustering, pp. 564\u2013565. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_426","DOI":"10.1007\/978-0-387-30164-8_426"},{"key":"3085_CR48","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1002\/9780470316801.ch2","volume":"344","author":"L Kaufman","year":"1990","unstructured":"Kaufman L (1990) Partitioning around medoids (program pam). Wiley Ser Probab Stat 344:68\u2013125","journal-title":"Wiley Ser Probab Stat"},{"key":"3085_CR49","doi-asserted-by":"crossref","unstructured":"Schubert E, Rousseeuw PJ (2019) Faster k-medoids clustering: improving the pam, clara, and clarans algorithms. In: International conference on similarity search and applications, pp. 171\u2013187. Springer","DOI":"10.1007\/978-3-030-32047-8_16"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03085-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03085-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03085-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:34:11Z","timestamp":1781152451000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03085-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":49,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["3085"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03085-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]},"assertion":[{"value":"28 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"340"}}