DEA를 이용한 의사결정단위의 클러스터링
Clustering of Decision Making Units using DEA
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초록
The conventional clustering approaches are mostly based on minimizing total dissimilarity of input and output. However,the clustering approach may not be helpful in some cases of clustering decision making units (DMUs) with production featureconverting multiple inputs into multiple outputs because it does not care converting functions. Data envelopment analysis (DEA)has been widely applied for efficiency estimation of such DMUs since it has non-parametric characteristics. We propose a newclustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world exampleis given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result andthe result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-meansalgorithm, we calculate new similarity value and compare it with the previous one.
Abstract
The conventional clustering approaches are mostly based on minimizing total dissimilarity of input and output. However,the clustering approach may not be helpful in some cases of clustering decision making units (DMUs) with production featureconverting multiple inputs into multiple outputs because it does not care converting functions. Data envelopment analysis (DEA)has been widely applied for efficiency estimation of such DMUs since it has non-parametric characteristics. We propose a newclustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world exampleis given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result andthe result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-meansalgorithm, we calculate new similarity value and compare it with the previous one.
참고문헌 (22)
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