Development of Efficient Data Mining Techniques for Fraud Identification

This study examine the credit card fraud problem and adopt some actual transactional data with an online questionnaire transaction data to identify and prevent fraud. The ultimate aim of this study is to compare the effectiveness of generating personalized classification model to represent the spend...

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Bibliographic Details
Main Author:	Alowais, Mohammed Ibrahim
Format:	Thesis
Published:	2012
Subjects:	HV Social pathology Social and public welfare Criminology
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id	my-mmu-ep.3652
record_format	uketd_dc
spelling	my-mmu-ep.36522012-12-03T01:26:33Z Development of Efficient Data Mining Techniques for Fraud Identification 2012-02 Alowais, Mohammed Ibrahim HV Social pathology. Social and public welfare. Criminology This study examine the credit card fraud problem and adopt some actual transactional data with an online questionnaire transaction data to identify and prevent fraud. The ultimate aim of this study is to compare the effectiveness of generating personalized classification model to represent the spending behavior of individuals in identifying fraud as compared to the general classification model constructed from the mass data collected from all individuals. 2012-02 Thesis http://shdl.mmu.edu.my/3652/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Computing and Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	HV Social pathology Social and public welfare Criminology
spellingShingle	HV Social pathology Social and public welfare Criminology Alowais, Mohammed Ibrahim Development of Efficient Data Mining Techniques for Fraud Identification
description	This study examine the credit card fraud problem and adopt some actual transactional data with an online questionnaire transaction data to identify and prevent fraud. The ultimate aim of this study is to compare the effectiveness of generating personalized classification model to represent the spending behavior of individuals in identifying fraud as compared to the general classification model constructed from the mass data collected from all individuals.
format	Thesis
qualification_level	Master's degree
author	Alowais, Mohammed Ibrahim
author_facet	Alowais, Mohammed Ibrahim
author_sort	Alowais, Mohammed Ibrahim
title	Development of Efficient Data Mining Techniques for Fraud Identification
title_short	Development of Efficient Data Mining Techniques for Fraud Identification
title_full	Development of Efficient Data Mining Techniques for Fraud Identification
title_fullStr	Development of Efficient Data Mining Techniques for Fraud Identification
title_full_unstemmed	Development of Efficient Data Mining Techniques for Fraud Identification
title_sort	development of efficient data mining techniques for fraud identification
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics
publishDate	2012
_version_	1747829535718506496

Development of Efficient Data Mining Techniques for Fraud Identification

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