지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제16권 제4호
- 발행연도
- 2014.1
- 수록면
- 1,727 - 1,734 (8page)
이용수
초록· 키워드
오류제보하기
Rule evaluation is one of the most popular techniques and plays an important role in data mining. A lot of measures have been proposed in different fields that try to evaluate features of the rules obtained by different types of association and classification tasks. The most frequently used rule evaluation measures are rule accuracy, sensitivity, relative accuracy, and relative sensitivity. In this paper, we propose a symmetrically relative accuracy to compensate the shortcomings of rule accuracy, sensitivity, relative accuracy, and relative sensitivity. This measure is divided the sum of rule accuracy and sensitivity to the sum of relative accuracy and relative sensitivity. It is a symmetric measure whose value is not changed by the position of antecedent and consequent item. And then we investigated the conditions of interestingness measures by Piatetsky-Shapiro (1991), and compared some properties of rule accuracy, sensitivity, relative accuracy, relative sensitivity, and symmetrically relative accuracy. The results showed that the symmetrically relative accuracy was more meaningful rule evaluation measure because it was symmetric, and monotonically increased as co-occurrence frequency increased.
목차
등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
Abudawood, T. / 2009 / Evaluation measures for multi-class subgroup discovery / Proceedings of European Conference on Machine Learning : 35 ~ 50
Agrawal, R. / 1993 / Mining association rules between sets of items in large databases / Proceedings of the ACM SIGMOD Conference on Management of Data : 207 ~ 216
Agrawal, R. / 1994 / Fast algorithms for mining association rules / Proceedings of the 20th VLDB Conference : 487 ~ 499
Bayardo, R. J. / 1998 / Efficiently mining long patterns from databases / Proceedings of ACM SIGMOD Conference on Management of Data : 85 ~ 93
Berzal, F. / 2001 / A new framework to assess association rules / Proceedings of the 4th International Conference on Intelligent Data Analysis : 95 ~ 104
Agrawal, R. / 1993 / Mining association rules between sets of items in large databases / Proceedings of the ACM SIGMOD Conference on Management of Data : 207 ~ 216
Agrawal, R. / 1994 / Fast algorithms for mining association rules / Proceedings of the 20th VLDB Conference : 487 ~ 499
Bayardo, R. J. / 1998 / Efficiently mining long patterns from databases / Proceedings of ACM SIGMOD Conference on Management of Data : 85 ~ 93
Berzal, F. / 2001 / A new framework to assess association rules / Proceedings of the 4th International Conference on Intelligent Data Analysis : 95 ~ 104
이 논문의 저자 정보
최근 본 자료
댓글(0)
0