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Best Deals & eBook Download An Improved Unsupervised Modeling Methodology For Detecting Fraud In Vendor Payment Transactions by Gregory W. Rouillard

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Gregory W. Rouillard
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This is a NAVAL POSTGRADUATE SCHOOL MONTEREY CA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A995714. The abstract provided by the Pentagon follows: In this thesis, we propose a standardized procedure for detecting fraud in Defense Finance and Accounting Service (DFAS) vendor payment transactions through Unsupervised Modeling (cluster analysis) . Clementine Data Mining software is used to construct unsupervised models of vendor payment data using the K-Means, Two Step, and Kohonen algorithms. Cluster validation techniques are applied to select the most useful model of each type, which are then combined to select candidate records for physical examination by a DFAS auditor. Our unsupervised modeling technique utilizes all the available valid transaction data, much of which is not admitted under the current supervised modeling procedure. Our procedure standardizes and provides rigor to the existing unsupervised modeling methodology at DFAS. Additionally, we demonstrate a new clustering approach called Tree Clustering, which uses Classification and Regression Trees to cluster data with automatic variable selection and scaling. A standardized procedure for Unsupervised Modeling, detailed explanation of all Clementine procedures, and implementation of the Tree Clustering algorithm are included as appendices.


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An Improved Unsupervised Modeling Methodology For Detecting Fraud In Vendor Payment Transactions by Gregory W. Rouillard

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