{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:49:41Z","timestamp":1772642981524,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01111-y","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T07:04:37Z","timestamp":1741331077000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Discovering customer segments through interaction behaviors for home appliance business"],"prefix":"10.1186","volume":"12","author":[{"given":"Youngjung","family":"Suh","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"issue":"3","key":"1111_CR1","first-page":"157","volume":"14","author":"KH Ahn","year":"2009","unstructured":"Ahn KH, Limg BH, Lee YH. The study of the selection of optimal variables and clustering method for the market segmentation. J Mark Manage Res. 2009;14(3):157\u201376.","journal-title":"J Mark Manage Res"},{"key":"1111_CR2","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1002\/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G","volume":"17","author":"DJ Ketchen","year":"1996","unstructured":"Ketchen DJ, Shook CL. The application of cluster analysis in strategic management research: an analysis and critique. Strateg Manag J. 1996;17:441\u201358.","journal-title":"Strateg Manag J"},{"key":"1111_CR3","doi-asserted-by":"crossref","unstructured":"Nabeel Mustafa SM, Akhtar A, Peter Noronha JT, Salman M, Baig MA. Customer segmentation using machine learning techniques. in 2023 international multi-disciplinary conference in emerging research trends (IMCERT) 2023; I:1\u20137.","DOI":"10.1109\/IMCERT57083.2023.10075194"},{"key":"1111_CR4","doi-asserted-by":"publisher","unstructured":"A, RS, Jaiswal A., PS. L S. Customer segmentation using machine learning. in 2023 third international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT) 2023;1\u20135 https:\/\/doi.org\/10.1109\/ICAECT57570.2023.10117924.","DOI":"10.1109\/ICAECT57570.2023.10117924"},{"key":"1111_CR5","doi-asserted-by":"publisher","unstructured":"U Sharma, G Aditi, NR Roy and SN Singh. Analysis of Customer Segmentation Clustering Techniques. 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2022; 374\u2013379, https:\/\/doi.org\/10.1109\/Confluence52989.2022.9734147.","DOI":"10.1109\/Confluence52989.2022.9734147"},{"key":"1111_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2014.02.137","volume":"275","author":"G Kou","year":"2014","unstructured":"Kou G, Peng Y, Wang G. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci. 2014;275:1\u201312. https:\/\/doi.org\/10.1016\/j.ins.2014.02.137.","journal-title":"Inf Sci"},{"key":"1111_CR7","doi-asserted-by":"publisher","unstructured":"Tang, G., Tian, R., & Wu, B. An Overview of Clustering Methods in The Financial World. Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022). https:\/\/doi.org\/10.2991\/aebmr.k.220307.084.","DOI":"10.2991\/aebmr.k.220307.084"},{"key":"1111_CR8","doi-asserted-by":"publisher","unstructured":"Z. Borbora, J. Srivastava, K.-W. Hsu, and D. Williams. Churn Prediction in MMORPGS Using Player Motivation Theories and an Ensemble Approach. In Proceedings of IEEE Third International Conference on Social Computing, 2011. https:\/\/doi.org\/10.1109\/PASSAT\/SocialCom.2011.122.","DOI":"10.1109\/PASSAT\/SocialCom.2011.122"},{"key":"1111_CR9","unstructured":"Market segmentation the bedrock of successful marketing. John Wiley & Sons, Ltd, 2012."},{"issue":"7","key":"1111_CR10","doi-asserted-by":"publisher","first-page":"5259","DOI":"10.1016\/j.eswa.2009.12.070","volume":"37","author":"SMS Hosseini","year":"2010","unstructured":"Hosseini SMS, Maleki A. Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Syst Appl. 2010;37(7):5259\u201364. https:\/\/doi.org\/10.1016\/j.eswa.2009.12.070.","journal-title":"Expert Syst Appl"},{"key":"1111_CR11","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1016\/j.jksuci.2018.09.004","volume":"33","author":"AJ Christy","year":"2021","unstructured":"Christy AJ, Umamakeswari A, Priyatharsini L, Neyaa A. RFM ranking an effective approach to customer segmentation. J King Saud Univ Comput Inf Sci. 2021;33:1251\u20137. https:\/\/doi.org\/10.1016\/j.jksuci.2018.09.004.","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"1111_CR12","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s10257-023-00640-4","volume":"21","author":"M Alves Gomes","year":"2023","unstructured":"Alves Gomes M, Meisen T. A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Inf Syst E-Bus Manage. 2023;21:527\u201370. https:\/\/doi.org\/10.1007\/s10257-023-00640-4.","journal-title":"Inf Syst E-Bus Manage"},{"key":"1111_CR13","doi-asserted-by":"publisher","DOI":"10.14569\/IJARAI.2015.041007","author":"CP Ezenkwu","year":"2015","unstructured":"Ezenkwu CP, Ozuomba S, Kalu C. Application of K-Means algorithm for efficient customer segmentation: a strategy for targeted customer services. Int J Adv Res Artif Intell. 2015. https:\/\/doi.org\/10.14569\/IJARAI.2015.041007.","journal-title":"Int J Adv Res Artif Intell"},{"key":"1111_CR14","doi-asserted-by":"publisher","first-page":"7785","DOI":"10.1007\/s00500-021-05796-0","volume":"25","author":"SP Nguyen","year":"2021","unstructured":"Nguyen SP. Deep customer segmentation with applications to a Vietnamese supermarkets\u2019 data. Soft Comput. 2021;25:7785\u201393. https:\/\/doi.org\/10.1007\/s00500-021-05796-0.","journal-title":"Soft Comput"},{"key":"1111_CR15","volume-title":"Mall Customer Segmentation Using Clustering Algorithm","author":"A Ishantha","year":"2021","unstructured":"Ishantha A. Mall Customer Segmentation Using Clustering Algorithm. Lnbti Machine Learning conference: Colombo, March; 2021."},{"issue":"4","key":"1111_CR16","first-page":"41","volume":"16","author":"SH Hur","year":"2009","unstructured":"Hur SH, Ryoo SY, Jeon SH. Determinants of online review adoption: focusing on online review quality and consensus. J Inf Technol Applic Manage. 2009;16(4):41\u201358.","journal-title":"J Inf Technol Applic Manage"},{"issue":"1","key":"1111_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.2307\/20721420","volume":"34","author":"SM Mudambi","year":"2010","unstructured":"Mudambi SM, Schuff D. Research note: What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Q. 2010;34(1):185\u2013200.","journal-title":"MIS Q"},{"key":"1111_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2023.102641","author":"J Joung","year":"2023","unstructured":"Joung J, Kim H. Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews. Int J Inf Manag. 2023. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2023.102641.","journal-title":"Int J Inf Manag"},{"key":"1111_CR19","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ijhm.2019.01.003","volume":"80","author":"A Ahani","year":"2019","unstructured":"Ahani A, Nilashi M, Ibrahim O, Sanzogni L, Weaven S. Market segmentation and travel choice prediction in spa hotels through tripadvisor\u2019s online reviews. Int J Hosp Manag. 2019;80:52\u201377.","journal-title":"Int J Hosp Manag"},{"key":"1111_CR20","doi-asserted-by":"publisher","first-page":"7068","DOI":"10.1080\/00207543.2019.1574989","volume":"57","author":"JW Bi","year":"2019","unstructured":"Bi JW, Liu Y, Fan ZP, Cambria E. Modelling customer satisfaction from online reviews using ensemble neural network and effect-based kano model. Int J Prod Res. 2019;57:7068\u201388.","journal-title":"Int J Prod Res"},{"key":"1111_CR21","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1002\/pra2.11","volume":"56","author":"BJ Jansen","year":"2019","unstructured":"Jansen BJ, Jung S-G, Salminen J. Capturing the change in topical interests of personas over time. Proc Assoc Inf Sci Technol. 2019;56:127\u201336. https:\/\/doi.org\/10.1002\/pra2.11.","journal-title":"Proc Assoc Inf Sci Technol"},{"key":"1111_CR22","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc4030021","author":"D Spiliotopoulos","year":"2020","unstructured":"Spiliotopoulos D, Margaris D, Vassilakis C. Data-assisted persona construction using social media data. Big Data Cogn Comput. 2020. https:\/\/doi.org\/10.3390\/bdcc4030021.","journal-title":"Big Data Cogn Comput"},{"key":"1111_CR23","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2021.1908670","author":"J Salminen","year":"2021","unstructured":"Salminen J, Guan K, Jung SG, Jansen BJ. A survey of 15 years of data-driven persona development. Int J Human-Comput Interact. 2021. https:\/\/doi.org\/10.1080\/10447318.2021.1908670.","journal-title":"Int J Human-Comput Interact"},{"issue":"4","key":"1111_CR24","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1089\/big.2021.0177","volume":"10","author":"J Salminen","year":"2022","unstructured":"Salminen J, Chhirang K, Jung S-G, Kathleen STh, Guan W, Jansen BJ. Big data, small personas: how algorithms shape the demographic representation of data-driven user segments. Big Data. 2022;10(4):313\u201336.","journal-title":"Big Data"},{"key":"1111_CR25","doi-asserted-by":"crossref","unstructured":"Salminen J, Guan K, Jung SG, and Jansen BJ. (2022) Use Cases for Design Personas: A Systematic Review and New Frontiers. 2022 ACM Conference on Human Factors in Computing Systems (CHI'22). New Orleans: USA.","DOI":"10.1145\/3491102.3517589"},{"key":"1111_CR26","doi-asserted-by":"publisher","unstructured":"CS Reddy, NSK Deepak Rao, A Sisir, VS Srinivasa Raju and SS Aravinth, \"A Comparative Survey on K-Means and Hierarchical Clustering in E-Commerce Systems,\" 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023. https:\/\/doi.org\/10.1109\/IDCIoT56793.2023.10053472.","DOI":"10.1109\/IDCIoT56793.2023.10053472"},{"key":"1111_CR27","doi-asserted-by":"publisher","unstructured":"Chalapathy N, VL H. 2022 Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company. Proceedings of the 4th International Conference on Information Management & Machine Intelligence. https:\/\/doi.org\/10.1145\/3590837.3590937.","DOI":"10.1145\/3590837.3590937"},{"key":"1111_CR28","doi-asserted-by":"publisher","unstructured":"Xiong, X. B2C E-Commerce Logistics Network optimization Based on Constrained Clustering Algorithm. 2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 1\u20136. https:\/\/doi.org\/10.1109\/GCAT59970.2023.10353469.","DOI":"10.1109\/GCAT59970.2023.10353469"},{"key":"1111_CR29","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10100-022-00802-8","volume":"31","author":"A Peri\u0161i\u0107","year":"2023","unstructured":"Peri\u0161i\u0107 A, Pahor M. Clustering mixed-type player behavior data for churn prediction in mobile games. Cent Eur J Oper Res. 2023;31:165\u201390. https:\/\/doi.org\/10.1007\/s10100-022-00802-8.","journal-title":"Cent Eur J Oper Res"},{"key":"1111_CR30","unstructured":"A. Bagga. The Emergence of Games As A Service. Industry Report, ThinkEquity LLC, Sn Francisco, May 4, 2009."},{"key":"1111_CR31","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/jtaer17010003","volume":"17","author":"A Scutariu","year":"2021","unstructured":"Scutariu A, \u0218u\u015fu \u0218, Huidumac-Petrescu C, Gogonea R. A cluster analysis concerning the behavior of enterprises with e-commerce activity in the context of the COVID-19 pandemic. J Theor Appl Electron Commer Res. 2021;17:47\u201368. https:\/\/doi.org\/10.3390\/jtaer17010003.","journal-title":"J Theor Appl Electron Commer Res"},{"key":"1111_CR32","doi-asserted-by":"publisher","first-page":"340","DOI":"10.3390\/jtaer19010018","volume":"19","author":"RM Gogonea","year":"2024","unstructured":"Gogonea RM, Moraru LC, Bodislav DA, P\u0103unescu LM, Vl\u0103sceanu CF. Similarities and disparities of e-commerce in the european union in the post-pandemic period. J Theor Appl Electron Commer Res. 2024;19:340\u201361. https:\/\/doi.org\/10.3390\/jtaer19010018.","journal-title":"J Theor Appl Electron Commer Res"},{"key":"1111_CR33","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.eswa.2016.09.016","volume":"66","author":"H Lorentz","year":"2016","unstructured":"Lorentz H, Hilmola O, Malmsten J, Srai J. Cluster analysis application for understanding SME manufacturing strategies. Expert Syst Appl. 2016;66:176\u201388. https:\/\/doi.org\/10.1016\/j.eswa.2016.09.016.","journal-title":"Expert Syst Appl"},{"key":"1111_CR34","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2307\/1247695","volume":"21","author":"W Smith","year":"1956","unstructured":"Smith W. Product differentiation and market segmentation as alternative marketing strategies. J Mark. 1956;21:3\u20138. https:\/\/doi.org\/10.2307\/1247695.","journal-title":"J Mark"},{"issue":"3","key":"1111_CR35","doi-asserted-by":"publisher","first-page":"317","DOI":"10.2307\/3150580","volume":"15","author":"Y Wind","year":"1978","unstructured":"Wind Y. Issues and advances in segmentation theory. J Mark Res. 1978;15(3):317\u201337. https:\/\/doi.org\/10.2307\/3150580.","journal-title":"J Mark Res"},{"issue":"14","key":"1111_CR36","first-page":"53","volume":"4","author":"HI Kwon","year":"2008","unstructured":"Kwon HI, Choi YS. A study on on-line game market segmentation classification and discrimination variable. J Korean Soc Comput Game. 2008;4(14):53\u201361.","journal-title":"J Korean Soc Comput Game"},{"key":"1111_CR37","first-page":"122","volume":"13","author":"N Hicham","year":"2022","unstructured":"Hicham N, Karim S. Analysis of unsupervised machine learning techniques for an efficient customer segmentation using clustering ensemble and spectral clustering. Int J Adv Comput Sci Appl. 2022;13:122\u201330.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"1111_CR38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.11113\/ijic.v12n1.325","volume":"12","author":"U Firdaus","year":"2021","unstructured":"Firdaus U, Utama D. Development of bank\u2019s customer segmentation model based on rfm+ b approach. Int J Innov Comput Inf Cont. 2021;12:17\u201326.","journal-title":"Int J Innov Comput Inf Cont"},{"key":"1111_CR39","doi-asserted-by":"crossref","unstructured":"Hossain, A.S. Customer segmentation using centroid based and density based clustering algorithms. In Proceedings of the 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 7\u20139 December 2017; pp.1\u20136.","DOI":"10.1109\/EICT.2017.8275249"},{"key":"1111_CR40","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.ijcce.2024.04.002","volume":"5","author":"MdA Uddin","year":"2024","unstructured":"Uddin MdA, Alamin Talukder Md, Redwan Ahmed Md, Khraisat A, Ammar Alazab Md, Islam M, Aryal S, Jibon FA. Data-driven strategies for digital native market segmentation using clustering. Int J Cogni Comput Eng. 2024;5:178\u201391. https:\/\/doi.org\/10.1016\/j.ijcce.2024.04.002.","journal-title":"Int J Cogni Comput Eng"},{"key":"1111_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21903-5_8","volume-title":"Hierarchical Clustering","author":"F Nielsen","year":"2016","unstructured":"Nielsen F. Hierarchical Clustering. Cham: Springer; 2016."},{"key":"1111_CR42","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput Appl Math. 1987;20:53\u201365.","journal-title":"Comput Appl Math"},{"issue":"2\u20133","key":"1111_CR43","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1023\/A:1012801612483","volume":"17","author":"M Halkidi","year":"2001","unstructured":"Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J Intell Inf Syst. 2001;17(2\u20133):107\u201345.","journal-title":"J Intell Inf Syst"},{"issue":"301","key":"1111_CR44","doi-asserted-by":"publisher","first-page":"236","DOI":"10.2307\/2282967","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236\u201344. https:\/\/doi.org\/10.2307\/2282967.","journal-title":"J Am Stat Assoc"},{"key":"1111_CR45","unstructured":"Tandon, Rashish; Sra, Suvrit (September 13, 2010). Sparse nonnegative matrix approximation: new formulations and algorithms (PDF) (Report). Max Planck Institute for Biological Cybernetics. Technical Report No. 193."},{"issue":"3","key":"1111_CR46","doi-asserted-by":"publisher","first-page":"4176","DOI":"10.1016\/j.eswa.2008.04.003","volume":"36","author":"C Ching-Hsue","year":"2009","unstructured":"Ching-Hsue C, You-Shyang C. Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst Applic. 2009;36(3):4176\u201384. https:\/\/doi.org\/10.1016\/j.eswa.2008.04.003.","journal-title":"Expert Syst Applic"},{"issue":"3","key":"1111_CR47","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1057\/dbm.2012.17","volume":"19","author":"D Chen","year":"2012","unstructured":"Chen D, Sain S, Guo K. Data mining for the online retail industry: a case study of RFM model-based customer segmentation using data mining. J Database Mark Cust Strategy Manag. 2012;19(3):197\u2013208. https:\/\/doi.org\/10.1057\/dbm.2012.17.","journal-title":"J Database Mark Cust Strategy Manag"},{"issue":"4","key":"1111_CR48","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1108\/MIP-11-2016-0210","volume":"35","author":"S Peker","year":"2017","unstructured":"Peker S, Kocyigit A, Eren PE. LRFMP model for customer segmentation in the grocery retail industry: a case study. Mark Intell Plan. 2017;35(4):544\u201359. https:\/\/doi.org\/10.1108\/MIP-11-2016-0210.","journal-title":"Mark Intell Plan"},{"issue":"5","key":"1111_CR49","doi-asserted-by":"publisher","first-page":"5529","DOI":"10.1016\/j.eswa.2011.11.066","volume":"39","author":"J-T Wei","year":"2012","unstructured":"Wei J-T, Lin S-Y, Weng C-C, Wu H-H. A case study of applying LRFM model in market segmentation of a children\u2019s dental clinic. Expert Syst Appl. 2012;39(5):5529\u201333. https:\/\/doi.org\/10.1016\/j.eswa.2011.11.066.","journal-title":"Expert Syst Appl"},{"key":"1111_CR50","doi-asserted-by":"publisher","DOI":"10.30880\/IJIE.2019.11.03.018","author":"M Fitri","year":"2019","unstructured":"Fitri M, Sarifah S, Syed A, Zeratul I, Mohd Y, Fachrudin H, Tubagus MAA. Segmentation model of customer lifetime value in small and medium enterprise (SMEs) using K-means clustering and LRFM model. Int J Integrat Eng. 2019. https:\/\/doi.org\/10.30880\/IJIE.2019.11.03.018.","journal-title":"Int J Integrat Eng"},{"issue":"3","key":"1111_CR51","doi-asserted-by":"publisher","first-page":"97","DOI":"10.18080\/jtde.v12n3.978","volume":"12","author":"W Chun-Gee","year":"2024","unstructured":"Chun-Gee W, Gee-Kok T, Su-Cheng H. Exploring customer segmentation in e-commerce using RFM analysis with clustering techniques. Australian J Telecommun Digit Econ. 2024;12(3):97\u2013125. https:\/\/doi.org\/10.18080\/jtde.v12n3.978.","journal-title":"Australian J Telecommun Digit Econ"},{"key":"1111_CR52","doi-asserted-by":"publisher","DOI":"10.61132\/maeswara.v2i5.1283","author":"H Andy","year":"2024","unstructured":"Andy H, Nila RJ, Aji S, Army PP, Muhammad AAT. Leveraging the RFM model for customer segmentation in a software-as-a-service (SaaS) business using Python. Maeswara. 2024. https:\/\/doi.org\/10.61132\/maeswara.v2i5.1283.","journal-title":"Maeswara"},{"issue":"1\u20132","key":"1111_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42488-023-00085-x","volume":"5","author":"S Peker","year":"2023","unstructured":"Peker S, Kart \u00d6. Transactional data-based customer segmentation applying CRISP-DM methodology: a systematic review. J Data, Inf Manage. 2023;5(1\u20132):1\u201321.","journal-title":"J Data, Inf Manage"},{"issue":"3","key":"1111_CR54","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/1082-989X.9.3.386","volume":"9","author":"D Steinley","year":"2004","unstructured":"Steinley D. Properties of the hubert-arable adjusted rand index. Psychol Methods. 2004;9(3):386\u201396. https:\/\/doi.org\/10.1037\/1082-989X.9.3.386.","journal-title":"Psychol Methods"},{"issue":"1","key":"1111_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat-Theory Methods. 1974;3(1):1\u201327.","journal-title":"Commun Stat-Theory Methods"},{"issue":"18","key":"1111_CR56","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1080\/10447318.2021.1908670","volume":"37","author":"J Salminen","year":"2021","unstructured":"Salminen J, Guan K, Jung SG, Jansen BJ. A survey of 15 years of data-driven persona development. Int J Human-Comput Interact. 2021;37(18):1685\u2013708. https:\/\/doi.org\/10.1080\/10447318.2021.1908670.","journal-title":"Int J Human-Comput Interact"},{"key":"1111_CR57","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/3-540-28349-8_2","volume-title":"Grouping Multidimensional Data","author":"P Berkhin","year":"2006","unstructured":"Berkhin P. A survey of clustering data mining techniques. In: Kogan J, Nicholas C, Teboulle M, editors. Grouping Multidimensional Data. Springer, Berlin: Heidelberg; 2006. p. 25\u201371."},{"issue":"2","key":"1111_CR58","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/34.908974","volume":"23","author":"AM Martinez","year":"2001","unstructured":"Martinez AM, Kak AC. PCA versus LDA. IEEE Trans Pattern Anal Mach Intell. 2001;23(2):228\u201333. https:\/\/doi.org\/10.1109\/34.908974.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1111_CR59","volume-title":"Perceiving the Ukraine-Russia conflict: topic modeling and clustering on twitter data","author":"P Chang","year":"2023","unstructured":"Chang P, Yu Y, Sanders A, Munasinghe T. Perceiving the Ukraine-Russia conflict: topic modeling and clustering on twitter data. Athens, Greece: IEEE; 2023."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01111-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01111-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01111-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T07:04:46Z","timestamp":1741331086000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01111-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1111"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01111-y","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"13 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"57"}}