F-Measure is one of the external validity indexes for evaluating clustering results and has been widely used.Though it has clear advantage over other widely usedexternal measures such as Purity and Entropy, FMeasure has inherently been less sensitive than other validity indexes in some cases. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we define a new validity index based on F-Measure, called Fn-Measure and show that it can detect the difference in the cases that original F-Measure cannot detect the difference in clustering results.

1. 서론
 2. 외부기준의 방법들
  2.1 평가척도를 평가하는 기준
  2.2 외부기준의방법들
 3. 새로운 클러스터링 타당성 인덱스
 4. 사례연구
 5. 결론
 참고문헌

• Kyeongtaek Kim(Department of Industrial and Management Engineering, Hannam University) | 김경택