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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.8
- 수록면
- 3,850 - 3,856 (7page)
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초록· 키워드
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Association rule mining is one of the tasks of datamining and it has been extensively studied recently. As a consequence, several methods for extractin gas sociation rules have been developed during the last years. Most of the muse the supportand confidence framework to extract the association rules. Researches are able to extract strong rules using this framework. However these measures are not good enough to solve the quality problems of the rules. A new data mining method using Genetic Network Programming(GNP) has also been developed recently which uses the χ<SUP>2</SUP> (chi-squared) as a correlation measure and its effectiveness has been shown for different data sets[1][2]. To enhance the correlation degree and comprehensibility of association rule, several correlation measures including lift, χ<SUP>2</SUP>, all-confidence and cosineare studied in this paper when they are in corporated in the conventional GNP based mining algorithm. A comparison between the correlation measures is made in the simulations when they are incorporated separately in to the GNP based mining method. Finally, the association rules extracted using different correlation measures are applied to the classification problems and the prediction accuracies of them are evaluated.
목차
Abstract
1.INTRODUCTION
2.ASSOCIATION RULES
3.INTERESTINGNESS OF ASSOCIATION RULES
4.CORRELATION MEASURES
5.GENETIC NETWORK PROGRAMMING
6.METHODOLOGY
7.SIMULATION RESULTS
8.CONCLUSIONS AND FUTURE WORK
REFERENCES
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