Attributably Pure Increment Ratio for Exploration of Meaningful Association Rules

• 발행기관: 한국자료분석학회
• 저자명: Hee Chang Park
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.15 No.3, 1151~1160쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2013.06.30

영문 초록

The objective of KDD systems is to extract implicitly hidden, previously unknown, and potentially useful information and knowledge from databases. Association rule mining is the method to quantify the interesting relationship between a set of items in a large database, and has been applied in various fields. There are many interestingness measures as the criteria for evaluating association rules. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. In this paper, we proposed a attributably pure increment ratio to be able to interpret operationally as an objective interestingness measure. And then we investigated the conditions of interestingness measure.

목차

1. Introduction

2. Attributablly pure increment ratio

3. Numerical example

4. Conclusion

References

키워드

association rule attributably pure confidence compared confidence interestingness measure attributably pure increment ratio