{"title":"基于数据挖掘的商业银行特殊客户识别","authors":"Yunfeng Liu, Xiaohui Wang, Dongsheng Zhai","doi":"10.1109/IFITA.2010.337","DOIUrl":null,"url":null,"abstract":"The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer’s risk. In this paper, four types of abnormal data detection method is introduced, present a new method - the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal customer","PeriodicalId":393802,"journal":{"name":"2010 International Forum on Information Technology and Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Commercial Banks Exceptional Client Distinguish Based on Data Mining\",\"authors\":\"Yunfeng Liu, Xiaohui Wang, Dongsheng Zhai\",\"doi\":\"10.1109/IFITA.2010.337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer’s risk. In this paper, four types of abnormal data detection method is introduced, present a new method - the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal customer\",\"PeriodicalId\":393802,\"journal\":{\"name\":\"2010 International Forum on Information Technology and Applications\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Forum on Information Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IFITA.2010.337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Forum on Information Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFITA.2010.337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Commercial Banks Exceptional Client Distinguish Based on Data Mining
The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer’s risk. In this paper, four types of abnormal data detection method is introduced, present a new method - the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal customer
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