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- W1920954502 abstract "Historical information on credit card transactions can be used to generate a fraud score which can then be used to reduce credit card fraud. The report describes a fraud-nonfraud classification methodology using a radial basis function network (RBFN) with a density based clustering approach. The input data is transformed into the cardinal component space and clustering as well as RBFN modeling is done using a few cardinal components. The methodology has been tested on a fraud detection problem and the preliminary results obtained are satisfactory." @default.
- W1920954502 created "2016-06-24" @default.
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- W1920954502 date "2002-12-23" @default.
- W1920954502 modified "2023-09-27" @default.
- W1920954502 title "Density-based clustering and radial basis function modeling to generate credit card fraud scores" @default.
- W1920954502 cites W2011620716 @default.
- W1920954502 doi "https://doi.org/10.1109/cifer.1996.501848" @default.
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