{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:20:12Z","timestamp":1781659212346,"version":"3.54.5"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents\u2019 investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.<\/jats:p>","DOI":"10.1186\/s40537-021-00529-4","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T10:04:26Z","timestamp":1635847466000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis"],"prefix":"10.1186","volume":"8","author":[{"given":"Tibor","family":"Kov\u00e1cs","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-4408","authenticated-orcid":false,"given":"Asefeh","family":"Asemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"issue":"6","key":"529_CR1","first-page":"1236","volume":"24","author":"RJ Sabhaya","year":"2020","unstructured":"Sabhaya RJ. An analysis of investment pattern of people during the period of 2018\u201319 in surat city. Int J Psychosoc Rehabil. 2020;24(6):1236\u201346.","journal-title":"Int J Psychosoc Rehabil"},{"key":"529_CR2","doi-asserted-by":"publisher","DOI":"10.1057\/dbm.2011.7","author":"N Woodcock","year":"2011","unstructured":"Woodcock N, Green A, Starkey M. Social CRM as a business strategy. J Database Mark Cust Strategy Manag. 2011. https:\/\/doi.org\/10.1057\/dbm.2011.7.","journal-title":"J Database Mark Cust Strategy Manag"},{"key":"529_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.12.068","author":"\u00c1 Tejeda-Lorente","year":"2019","unstructured":"Tejeda-Lorente \u00c1, Bernab\u00e9-Moreno J, Herce-Zelaya J, Porcel C, Herrera-Viedma E. A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds. Proc CompSci. 2019. https:\/\/doi.org\/10.1016\/j.procs.2019.12.068.","journal-title":"Proc CompSci"},{"key":"529_CR4","volume-title":"Online consumer behavior: theory and research in social media, advertising, and e-tail","year":"2012","unstructured":"Scheinbaum A, editor. Online consumer behavior: theory and research in social media, advertising, and e-tail. New York: Routledge\/Taylor & Francis Group; 2012."},{"key":"529_CR5","doi-asserted-by":"publisher","DOI":"10.1108\/JCM-08-2016-1908","author":"D Arli","year":"2017","unstructured":"Arli D. Investigating consumer ethics: a segmentation study. JCM. 2017. https:\/\/doi.org\/10.1108\/JCM-08-2016-1908.","journal-title":"JCM"},{"key":"529_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/S0148-2963(96)00032-X","author":"G Barczak","year":"1997","unstructured":"Barczak G, Ellen PS, Pilling BK. Developing typologies of consumer motives for use of technologically based banking services. J Bus Res. 1997. https:\/\/doi.org\/10.1016\/S0148-2963(96)00032-X.","journal-title":"J Bus Res"},{"key":"529_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/S0148-2963(98)00060-5","author":"AD Athanassopoulos","year":"2000","unstructured":"Athanassopoulos AD. Customer satisfaction cues to support market segmentation and explain switching behavior. J Bus Res. 2000. https:\/\/doi.org\/10.1016\/S0148-2963(98)00060-5.","journal-title":"J Bus Res"},{"key":"529_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2014.02.019","author":"A Persson","year":"2014","unstructured":"Persson A, Ryals L. Making customer relationship decisions: analytics v rules of thumb. J Bus Res. 2014. https:\/\/doi.org\/10.1016\/j.jbusres.2014.02.019.","journal-title":"J Bus Res"},{"key":"529_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.08.025","author":"S Hwang","year":"2020","unstructured":"Hwang S, Kim J, Park E, Kwon SJ. Who will be your next customer: a machine learning approach to customer return visits in airline services. J Bus Res. 2020. https:\/\/doi.org\/10.1016\/j.jbusres.2020.08.025.","journal-title":"J Bus Res"},{"key":"529_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.11.001","author":"SMC Loureiro","year":"2020","unstructured":"Loureiro SMC, Guerreiro J, Tussyadiah I. Artificial intelligence in business: state of the art and future research agenda. J Bus Res. 2020. https:\/\/doi.org\/10.1016\/j.jbusres.2020.11.001.","journal-title":"J Bus Res"},{"key":"529_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.07.039","author":"E Calderon-Monge","year":"2020","unstructured":"Calderon-Monge E, Pastor-Sanz I, Sendra Garcia FJ. Analysis of sustainable consumer behavior as a business opportunity. J Bus Res. 2020. https:\/\/doi.org\/10.1016\/j.jbusres.2020.07.039.","journal-title":"J Bus Res"},{"key":"529_CR12","doi-asserted-by":"publisher","unstructured":"V. P. Semenov, V. v. Chernokulsky, and N. v. Razmochaeva, Research of artificial intelligence in the retail management problems. 2017. doi: https:\/\/doi.org\/10.1109\/CTSYS.2017.8109560.","DOI":"10.1109\/CTSYS.2017.8109560"},{"key":"529_CR13","doi-asserted-by":"publisher","first-page":"847","DOI":"10.22434\/IFAMR2017.0033","volume":"21","author":"G Soltani-Fesaghandis","year":"2018","unstructured":"Soltani-Fesaghandis G, Pooya A. Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry. Int Food Agribusiness Manag Rev. 2018;21:847\u201364.","journal-title":"Int Food Agribusiness Manag Rev"},{"key":"529_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.05.027","author":"J Burez","year":"2009","unstructured":"Burez J, van den Poel D. Handling class imbalance in customer churn prediction. Expert Syst Appl. 2009. https:\/\/doi.org\/10.1016\/j.eswa.2008.05.027.","journal-title":"Expert Syst Appl"},{"key":"529_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/BF00337288","author":"T Kohonen","year":"1982","unstructured":"Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern. 1982. https:\/\/doi.org\/10.1007\/BF00337288.","journal-title":"Biol Cybern"},{"issue":"9","key":"529_CR16","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen T. The self-organizing map. Proc IEEE. 1990;78(9):1464\u201380. https:\/\/doi.org\/10.1109\/5.58325.","journal-title":"Proc IEEE"},{"key":"529_CR17","unstructured":"S. Sinclair and G. Rockwell, \u201cVoyant Tools,\u201d 2016. http:\/\/voyant-tools.org\/. Accessed 05 Jan 2021."},{"key":"529_CR18","unstructured":"\u201cYewno,\u201d 2020. https:\/\/discover.yewno.com\/. Accessed 05 Jan 2021."},{"key":"529_CR19","unstructured":"A. C. Edmondson and S. E. Mcmanus, Methodological fit in management field research, 2007. [Online]. https:\/\/www.jstor.org\/stable\/20159361"},{"key":"529_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2019.08.003","author":"T Keiningham","year":"2020","unstructured":"Keiningham T, et al. Customer experience driven business model innovation. J Bus Res. 2020. https:\/\/doi.org\/10.1016\/j.jbusres.2019.08.003.","journal-title":"J Bus Res"},{"key":"529_CR21","doi-asserted-by":"publisher","DOI":"10.2501\/IJMR-53-6-771-792","author":"S Maklan","year":"2011","unstructured":"Maklan S, Klaus P. Customer Experience: Are We Measuring the Right Things? Int J Market Res. 2011. https:\/\/doi.org\/10.2501\/IJMR-53-6-771-792.","journal-title":"Int J Market Res"},{"key":"529_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1057\/9781137375469","volume-title":"Measuring customer experience","author":"P Klaus","year":"2015","unstructured":"Klaus P. Customer experience: the origins and importance for your business. In: Klaus P, editor. Measuring customer experience. London: Palgrave Macmillan UK; 2015. p. 1\u201321 (10.1057\/9781137375469_1)."},{"key":"529_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.01.042","author":"VG Kuppelwieser","year":"2020","unstructured":"Kuppelwieser VG, Klaus P. Measuring customer experience quality: the EXQ scale revisited. J Bus Res. 2020. https:\/\/doi.org\/10.1016\/j.jbusres.2020.01.042.","journal-title":"J Bus Res"},{"key":"529_CR24","doi-asserted-by":"publisher","unstructured":"L. Wewege and M. C. Thomsett, The digital banking revolution: how fintech companies are transforming the retail banking industry through disruptive financial innovation. Walter de Gruyter GmbH & Co KG, 2019. https:\/\/doi.org\/10.1515\/9781547401598","DOI":"10.1515\/9781547401598"},{"key":"529_CR25","doi-asserted-by":"publisher","DOI":"10.1108\/JEIM-01-2020-0029","author":"M de Marco","year":"2021","unstructured":"de Marco M, Fantozzi P, Fornaro C, Laura L, Miloso A. Cognitive analytics management of the customer lifetime value: an artificial neural network approach. JEIM. 2021. https:\/\/doi.org\/10.1108\/JEIM-01-2020-0029.","journal-title":"JEIM"},{"key":"529_CR26","doi-asserted-by":"publisher","DOI":"10.33215\/sjom.v4i4.663","author":"A Fatima","year":"2021","unstructured":"Fatima A, Sharma JK. Segmenting Investors on their Biases Manifested in Investment Decision-Making by Individual Investors. SJOM. 2021. https:\/\/doi.org\/10.33215\/sjom.v4i4.663.","journal-title":"SJOM"},{"key":"529_CR27","unstructured":"J. J\u00e4\u00e4skel\u00e4inen, \u201cSegmentation of investor customers using machine learning in banking,\u201d Lappeenranta, 2021 [Online]. http:\/\/urn.fi\/URN:NBN:fi-fe2021051730210. Accessed 09 Sep 2021."},{"key":"529_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.iimb.2015.09.001","author":"KC Mishra","year":"2015","unstructured":"Mishra KC, Metilda MJ. A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias. IIMB Manag Rev. 2015. https:\/\/doi.org\/10.1016\/j.iimb.2015.09.001.","journal-title":"IIMB Manag Rev"},{"key":"529_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2015.11.351","author":"S Aren","year":"2015","unstructured":"Aren S, Aydemir SD. The factors influencing given investment choices of individuals. Proc Soc Behav Sci. 2015. https:\/\/doi.org\/10.1016\/j.sbspro.2015.11.351.","journal-title":"Proc Soc Behav Sci"},{"key":"529_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2014.11.038","author":"Y-L Lai","year":"2015","unstructured":"Lai Y-L, Lin F-J, Lin Y-H. Factors affecting firm\u2019s R&D investment decisions. J Bus Res. 2015. https:\/\/doi.org\/10.1016\/j.jbusres.2014.11.038.","journal-title":"J Bus Res"},{"key":"529_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2017.03.002","author":"CM Kuhnen","year":"2017","unstructured":"Kuhnen CM, Miu AC. Socioeconomic status and learning from financial information. J Financ Econ. 2017. https:\/\/doi.org\/10.1016\/j.jfineco.2017.03.002.","journal-title":"J Financ Econ"},{"key":"529_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2004.03.003","author":"DA Plath","year":"2005","unstructured":"Plath DA, Stevenson TH. Financial services consumption behavior across Hispanic American consumers. J Bus Res. 2005. https:\/\/doi.org\/10.1016\/j.jbusres.2004.03.003.","journal-title":"J Bus Res"},{"key":"529_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2006.05.008","author":"G Shim","year":"2008","unstructured":"Shim G, Lee S, Kim Y. How investor behavioral factors influence investment satisfaction, trust in investment company, and reinvestment intention. J Bus Res. 2008. https:\/\/doi.org\/10.1016\/j.jbusres.2006.05.008.","journal-title":"J Bus Res"},{"key":"529_CR34","doi-asserted-by":"publisher","unstructured":"T. Zhang, X. Huang, J. Tang, and X. Luo, Case study on cluster analysis of the telecom customers based on consumers\u2019 behavior. 2011. doi: https:\/\/doi.org\/10.1109\/ICIEEM.2011.6035407.","DOI":"10.1109\/ICIEEM.2011.6035407"},{"issue":"1","key":"529_CR35","first-page":"51","volume":"5","author":"AE Oprescu","year":"2014","unstructured":"Oprescu AE. The strategic marketing planning\u2014general framework for customer segmentation. Ann Spiru Haret Univ Econ Ser. 2014;5(1):51\u20139.","journal-title":"Ann Spiru Haret Univ Econ Ser"},{"key":"529_CR36","doi-asserted-by":"publisher","unstructured":"R. Ait Daoud, A. Amine, B. Bouikhalene, and R. Lbibb, Combining RFM model and clustering techniques for customer value analysis of a company selling online. 2015. doi: https:\/\/doi.org\/10.1109\/AICCSA.2015.7507238.","DOI":"10.1109\/AICCSA.2015.7507238"},{"key":"529_CR37","doi-asserted-by":"publisher","DOI":"10.35944\/jofrp.2019.8.1.008","author":"D Dhawan","year":"2019","unstructured":"Dhawan D, Mehta SK. Saving and investment pattern: assessment and prospects. ACRN J Finance Risk Perspect. 2019. https:\/\/doi.org\/10.35944\/jofrp.2019.8.1.008.","journal-title":"ACRN J Finance Risk Perspect"},{"key":"529_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2020.09.030","author":"A Higuchi","year":"2021","unstructured":"Higuchi A, Maehara R. A factor-cluster analysis profile of consumers. J Bus Res. 2021. https:\/\/doi.org\/10.1016\/j.jbusres.2020.09.030.","journal-title":"J Bus Res"},{"key":"529_CR39","doi-asserted-by":"publisher","unstructured":"P. Goncarovs, Using data analytics for customers segmentation: experimental study at a financial institution. 2018. doi: https:\/\/doi.org\/10.1109\/ITMS.2018.8552951.","DOI":"10.1109\/ITMS.2018.8552951"},{"key":"529_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm12040189","author":"RS Santos","year":"2019","unstructured":"Santos RS, Qin L. Risk capital and emerging technologies: innovation and investment patterns based on artificial intelligence patent data analysis. JRFM. 2019. https:\/\/doi.org\/10.3390\/jrfm12040189.","journal-title":"JRFM"},{"key":"529_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8116(02)00080-0","author":"DS Boone","year":"2002","unstructured":"Boone DS, Roehm M. Retail segmentation using artificial neural networks. Int J Mark Res. 2002. https:\/\/doi.org\/10.1016\/S0167-8116(02)00080-0.","journal-title":"Int J Mark Res"},{"key":"529_CR42","doi-asserted-by":"publisher","unstructured":"Ying Li and Feng Lin, Customer segmentation analysis based on SOM clustering. 2008. doi: https:\/\/doi.org\/10.1109\/SOLI.2008.4686353.","DOI":"10.1109\/SOLI.2008.4686353"},{"key":"529_CR43","doi-asserted-by":"publisher","unstructured":"Y. Li, Y. Wu, and F. Lin, Research on Customer Segmentation Based on a Two-Stage SOM Clustering Algorithm. 2009. doi: https:\/\/doi.org\/10.1109\/ICMSS.2009.5302076.","DOI":"10.1109\/ICMSS.2009.5302076"},{"key":"529_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-008-0226-y","author":"E Bign\u00e9","year":"2010","unstructured":"Bign\u00e9 E, Aldas-Manzano J, K\u00fcster I, Vila N. Mature market segmentation: a comparison of artificial neural networks and traditional methods. Neural Comput App. 2010. https:\/\/doi.org\/10.1007\/s00521-008-0226-y.","journal-title":"Neural Comput App"},{"key":"529_CR45","doi-asserted-by":"publisher","DOI":"10.5772\/50937","author":"MKY Mak","year":"2011","unstructured":"Mak MKY, Ho GTS, Ting SL. A financial data mining model for extracting customer behavior. Int J Eng Bus Manag. 2011. https:\/\/doi.org\/10.5772\/50937.","journal-title":"Int J Eng Bus Manag"},{"key":"529_CR46","first-page":"3","volume":"8","author":"MS Saluja","year":"2017","unstructured":"Saluja MS, Shaikh Y. Decoding investment pattern of fiis and diis in indian stock market using decision tree. IJACR. 2017;8:3.","journal-title":"IJACR"},{"key":"529_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2018.09.027","author":"T-H Chen","year":"2019","unstructured":"Chen T-H, Ho R-J, Liu Y-W. Investor personality predicts investment performance? A statistics and machine learning model investigation. Comput Hum Behav. 2019. https:\/\/doi.org\/10.1016\/j.chb.2018.09.027.","journal-title":"Comput Hum Behav"},{"key":"529_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2007.09.014","author":"N Albert","year":"2008","unstructured":"Albert N, Merunka D, Valette-Florence P. When consumers love their brands: exploring the concept and its dimensions. J Bus Res. 2008. https:\/\/doi.org\/10.1016\/j.jbusres.2007.09.014.","journal-title":"J Bus Res"},{"key":"529_CR49","doi-asserted-by":"publisher","DOI":"10.1108\/08858621111156412","author":"C Lamprinopoulou","year":"2011","unstructured":"Lamprinopoulou C, Tregear A. Inter-firm relations in SME clusters and the link to marketing performance. J Bus Ind Mark. 2011. https:\/\/doi.org\/10.1108\/08858621111156412.","journal-title":"J Bus Ind Mark"},{"key":"529_CR50","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1109\/ACCESS.2015.2477216","volume":"3","author":"D Lam","year":"2015","unstructured":"Lam D, Wei M, Wunsch D. Clustering data of mixed categorical and numerical type with unsupervised feature learning. IEEE Access. 2015;3:1605\u201316. https:\/\/doi.org\/10.1109\/ACCESS.2015.2477216.","journal-title":"IEEE Access"},{"issue":"5","key":"529_CR51","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/S0306-4379(00)00022-3","volume":"25","author":"S Guha","year":"2000","unstructured":"Guha S, Rastogi R, Shim K. Rock: a robust clustering algorithm for categorical attributes. Inf Syst. 2000;25(5):345\u201366. https:\/\/doi.org\/10.1016\/S0306-4379(00)00022-3.","journal-title":"Inf Syst"},{"key":"529_CR52","doi-asserted-by":"publisher","unstructured":"V. Ganti, J. Gehrke, and R. Ramakrishnan, CACTUS---clustering categorical data using summaries. 1999. doi: https:\/\/doi.org\/10.1145\/312129.312201.","DOI":"10.1145\/312129.312201"},{"key":"529_CR53","unstructured":"He Z, Xu X, Deng S. Clustering mixed numeric and categorical data: a cluster ensemble approach; 2005. arXiv:cs\/0509011."},{"issue":"11","key":"529_CR54","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1016\/S0305-0548(01)00043-0","volume":"29","author":"RJ Kuo","year":"2002","unstructured":"Kuo RJ, Ho LM, Hu CM. Integration of self-organizing feature map and K-means algorithm for market segmentation. Comput Oper Res. 2002;29(11):1475\u201393. https:\/\/doi.org\/10.1016\/S0305-0548(01)00043-0.","journal-title":"Comput Oper Res"},{"key":"529_CR55","unstructured":"R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Core Team; 2020. https:\/\/www.gbif.org\/tool\/81287\/r-a-language-and-environment-for-statistical-computing. Accessed 19 Dec 2020."},{"key":"529_CR56","unstructured":"A. Kassambara and F. Mundt, \u201cfactoextra: Extract and Visualize the Results of Multivariate Data Analyses,\u201d Apr. 01, 2020. http:\/\/www.sthda.com\/english\/rpkgs\/factoextra. Accessed 19 Dec 2020."},{"issue":"7","key":"529_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v087.i07","volume":"87","author":"R Wehrens","year":"2018","unstructured":"Wehrens R, Kruisselbrink J. Flexible self-organizing maps in kohonen 3.0. J Stat Softw. 2018;87(7):1\u201318. https:\/\/doi.org\/10.18637\/jss.v087.i07.","journal-title":"J Stat Softw"},{"issue":"5","key":"529_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v021.i05","volume":"21","author":"R Wehrens","year":"2007","unstructured":"Wehrens R, Buydens LMC. Self- and super-organizing maps in R: The kohonen package. J Stat Softw. 2007;21(5):1\u201319. https:\/\/doi.org\/10.18637\/jss.v021.i05.","journal-title":"J Stat Softw"},{"key":"529_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"J Comput Appl Math"},{"key":"529_CR60","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00293","author":"R Tibshirani","year":"2001","unstructured":"Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Series B Stat Methodol. 2001. https:\/\/doi.org\/10.1111\/1467-9868.00293.","journal-title":"J R Stat Soc Series B Stat Methodol"},{"key":"529_CR61","unstructured":"\u201cportfolio.hu traffic overview.\u201d https:\/\/www.similarweb.com\/website\/portfolio.hu\/. Accessed 31 Jan 2021."},{"key":"529_CR62","unstructured":"\u201cportfolio.hu conferences.\u201d https:\/\/www.portfolio.hu\/en\/events. Accessed 31 Jan 2021."},{"key":"529_CR63","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1950.tb00463.x","author":"RW Hamming","year":"1950","unstructured":"Hamming RW. Error detecting and error correcting codes. Bell Syst Tech J. 1950. https:\/\/doi.org\/10.1002\/j.1538-7305.1950.tb00463.x.","journal-title":"Bell Syst Tech J"},{"key":"529_CR64","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1002\/9780470316801.ch2","volume":"344","author":"L Kaufman","year":"1990","unstructured":"Kaufman L, Rousseeuw PJ. Partitioning around medoids (program pam). Find Grp Data. 1990;344:68\u2013125.","journal-title":"Find Grp Data"},{"key":"529_CR65","doi-asserted-by":"publisher","DOI":"10.1108\/JBIM-05-2015-0101","author":"M Els\u00e4\u00dfer","year":"2017","unstructured":"Els\u00e4\u00dfer M, Wirtz BW. Rational and emotional factors of customer satisfaction and brand loyalty in a business-to-business setting. JBIM. 2017. https:\/\/doi.org\/10.1108\/JBIM-05-2015-0101.","journal-title":"JBIM"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00529-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-021-00529-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00529-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T10:05:22Z","timestamp":1635847522000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-021-00529-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["529"],"URL":"https:\/\/doi.org\/10.1186\/s40537-021-00529-4","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"23 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human or animal participants performed by any of the authors.","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 of participation"}},{"value":"Work at the Corvinus University of Budapest helped design and develop the survey in conjunction with commercial companies (Dorsum and Portfolio).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"141"}}