원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.7
2016.07
pp.233-242
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Concerning the condition that there is a glittering array of disadvantages such as frequent candidate collection of Apriori algorithm, this paper comes up with cost-sensitive filtering matrix Apriori algorithm based on weighting. What’s more, with the help of FP-tree algorithm, we can carry out cost-sensitive learning through relevant data of its constructed decision tree to set different weighting for data and confidence level.
목차
Abstract
1. Introduction
2. Apriori Algorithm Based on Association Rules
2.1. Apriori Algorithm
2.2. Analysis of Algorithm Deficiency
3. Advanced Apriori Mining Algorithm
3.1. Cost-Sensitive Learning
3.2. Setting the Confidence Level of Weighting
3.3. Finding K-Frequency Set by Using Non-frequency Filter Matrix Set
3.4. Generation of Strong Association Rules
3.5 Initial Matrix Required by Non-frequency Filter Matrix Apriori Algorithm
4. Simulation Experiment
4.1. Application of the Present Algorithm
4.2. Comparison with Other Related Mining Algorithms
5. Conclusion
References
1. Introduction
2. Apriori Algorithm Based on Association Rules
2.1. Apriori Algorithm
2.2. Analysis of Algorithm Deficiency
3. Advanced Apriori Mining Algorithm
3.1. Cost-Sensitive Learning
3.2. Setting the Confidence Level of Weighting
3.3. Finding K-Frequency Set by Using Non-frequency Filter Matrix Set
3.4. Generation of Strong Association Rules
3.5 Initial Matrix Required by Non-frequency Filter Matrix Apriori Algorithm
4. Simulation Experiment
4.1. Application of the Present Algorithm
4.2. Comparison with Other Related Mining Algorithms
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보