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 feature converting 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 new clustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world example is given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result and the result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-means algorithm, we calculate new similarity value and compare it with the previous one.

1. 서 론
 2. DEA를 이용한 클러스터링
  2.1 DEA
  2.2 DEA 기반 클러스터링 알고리듬
 3. 적용 사례
  3.1 데이터
  3.2 결과
 4. 결 론
 References

• 김경택(한남대학교 산업경영공학과) | Kyeongtaek Kim Corresponding Author