{"title":"Mining and Exploration of Credit Cards Data in UAE","authors":"Sarween Zaza, M. Al-Emran","doi":"10.1109/ECONF.2015.57","DOIUrl":null,"url":null,"abstract":"Credit cards have become an essential element in the banking industry. Credit cards add a significant value for the banks. Mining credit cards can find interesting patterns among different variables that may be used in the future by the policy makers for building their future policy. In this study, we have investigated the credit card-holder's behavior in order to predict the market segmentation. An online questionnaire survey regarding credit card usage has been used for data collection. Two techniques have been applied on the collected data, Decision Trees and K-means through the use of training and testing sets. Results indicated how people are grouped based on their income which in turn will help in building the appropriate decision on which region needs to be targeted. Moreover, results revealed different work sectors for the credit card-holders and which type of credit card is used with regard to their income.","PeriodicalId":268471,"journal":{"name":"2015 Fifth International Conference on e-Learning (econf)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Fifth International Conference on e-Learning (econf)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECONF.2015.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
Credit cards have become an essential element in the banking industry. Credit cards add a significant value for the banks. Mining credit cards can find interesting patterns among different variables that may be used in the future by the policy makers for building their future policy. In this study, we have investigated the credit card-holder's behavior in order to predict the market segmentation. An online questionnaire survey regarding credit card usage has been used for data collection. Two techniques have been applied on the collected data, Decision Trees and K-means through the use of training and testing sets. Results indicated how people are grouped based on their income which in turn will help in building the appropriate decision on which region needs to be targeted. Moreover, results revealed different work sectors for the credit card-holders and which type of credit card is used with regard to their income.