Approved for public release; distribution is unlimited. / (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 Recommended SOP for Unsupervised Modeling, detailed explanation of all Clementine procedures, and implementation of the Tree Clustering algorithm are included as appendices. / Major, United States Marine Corps
| Identifer | oai:union.ndltd.org:nps.edu/oai:calhoun.nps.edu:10945/916 |
| Date | 06 1900 |
| Creators | Rouillard, Gregory W. |
| Contributors | Buttrey, Samuel E., Whitaker, Lyn R., Naval Postgraduate School (U.S.), Operations Research |
| Publisher | Monterey, California. Naval Postgraduate School |
| Source Sets | Naval Postgraduate School |
| Detected Language | English |
| Type | Thesis |
| Format | xxii, 149 p. : ill. (chiefly col.) ;, application/pdf |
| Rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted. |
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