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A Data-Mining-based Methodology for Military Occupational Specialty Assignment  

민규식 (고려대학교 공과대학원)
정지원 (고려대학교 공과대학)
최인찬 (고려대학교 공과대학원)
Publication Information
Journal of the military operations research society of Korea / v.30, no.1, 2004 , pp. 1-14 More about this Journal
Abstract
In this paper, we propose a new data-mining-based methodology for military occupational specialty assignment. The proposed methodology consists of two phases, feature selection and man-power assignment. In the first phase, the k-means partitioning algorithm and the optimal variable weighting algorithm are used to determine attribute weights. We address limitations of the optimal variable weighting algorithm and suggest a quadratic programming model that can handle categorical variables and non-contributory trivial variables. In the second phase, we present an integer programming model to deal with a man-power assignment problem. In the model, constraints on demand-supply requirements and training capacity are considered. Moreover, the attribute weights obtained in the first phase for each specialty are used to measure dissimilarity. Results of a computational experiment using real-world data are provided along with some analysis.
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