{"title":"应用聚类分析评估银行卡持卡人中诈骗受害者的比例","authors":"S. Alkhasov, Alexander Tselykh, A. Tselykh","doi":"10.1145/2799979.2800033","DOIUrl":null,"url":null,"abstract":"In this paper, we present a method for the assessment of the share of cardholders most prone to various types of bank fraud (i.e. fishing, vishing, skimming). For this purpose, a forecasting information system has been designed. It is based on a clustering module used for output of a certain set of cluster indices that depend on the percentage of aggrieved clients in the training sample. The k-means method is used for clustering. The initial coordinates of centroids are defined using advanced k-means++ algorithm.","PeriodicalId":293190,"journal":{"name":"Proceedings of the 8th International Conference on Security of Information and Networks","volume":"62 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Application of cluster analysis for the assessment of the share of fraud victims among bank card holders\",\"authors\":\"S. Alkhasov, Alexander Tselykh, A. Tselykh\",\"doi\":\"10.1145/2799979.2800033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a method for the assessment of the share of cardholders most prone to various types of bank fraud (i.e. fishing, vishing, skimming). For this purpose, a forecasting information system has been designed. It is based on a clustering module used for output of a certain set of cluster indices that depend on the percentage of aggrieved clients in the training sample. The k-means method is used for clustering. The initial coordinates of centroids are defined using advanced k-means++ algorithm.\",\"PeriodicalId\":293190,\"journal\":{\"name\":\"Proceedings of the 8th International Conference on Security of Information and Networks\",\"volume\":\"62 6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th International Conference on Security of Information and Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2799979.2800033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th International Conference on Security of Information and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2799979.2800033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of cluster analysis for the assessment of the share of fraud victims among bank card holders
In this paper, we present a method for the assessment of the share of cardholders most prone to various types of bank fraud (i.e. fishing, vishing, skimming). For this purpose, a forecasting information system has been designed. It is based on a clustering module used for output of a certain set of cluster indices that depend on the percentage of aggrieved clients in the training sample. The k-means method is used for clustering. The initial coordinates of centroids are defined using advanced k-means++ algorithm.
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