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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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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 제3호
- 발행연도
- 2014.1
- 수록면
- 1,141 - 1,149 (9page)
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초록· 키워드
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Today, government, public institutions and companies began to use data mining techniques to discover valuable information and knowledge from big database. Data mining is the process of analyzing data from different perspectives, and summarizing it into useful information through a huge volume database. Association rule, one of the well-studied methods in data mining, finds the relationship among itemsets in a massive database. In finding meaningful association rules, several objective interestingness measures are used, which are support, confidence, net confidence measure, and symmetrically pure confidence. But these measures are not sufficient to generate only interesting information, and have some drawbacks that they can not determine the direction of the association, and are difficult to interpret operationally. In this paper, we proposed a symmetrically weighted net confidence as an association threshold, and investigate the conditions of association criteria. Also, we compared this measure with some association thresholds through a few experiments. The results showed that the symmetrically weighted net confidence monotonically increased as co-occurrence frequency increased, had positive or negative values, and is symmetric.
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참고문헌 (28)
참고문헌 신청
Toivonen, H. / 1996 / Sampling large database for association rules / Proceedings of the 22nd VLDB Conference :134~145
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
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