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Out of all the techniques, Decision Tree is recommended for profiling studies because apart from obtaining high accurate results, it also yields \u2018if\u2013then\u2019 classification rules. The classification rules provided here can be used to target potential customers to adopt mobile banking by offering them appropriate incentives.<\/p>","DOI":"10.4018\/jisss.2013010105","type":"journal-article","created":{"date-parts":[[2013,3,6]],"date-time":"2013-03-06T17:05:13Z","timestamp":1362589513000},"page":"82-92","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning Techniques Applied to Profile Mobile Banking Users in India"],"prefix":"10.4018","volume":"5","author":[{"given":"M.","family":"Carr","sequence":"first","affiliation":[{"name":"Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India"}]},{"given":"V.","family":"Ravi","sequence":"additional","affiliation":[{"name":"Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India"}]},{"given":"G. 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