원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.4
2016.04
pp.87-94
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing.
목차
Abstract
1. Introduction
2. Description of Basic Algorithm
3. Apriori Algorithm based on Cost-Sensitive Non-frequent Filter Matrix
3.1 Cost-Sensitive Learning
3.2 Seek k –Frequency Set by Using Non-Frequency Filter Matrix
3.3 Generate Strong Correlation Rules
3.4 Generate Non-Frequent Filter Matrix
4. Data Mining in Cloud Computing
5. Analysis of Improved Apriori Algorithm in Cloud Computing
6. Conclusion
References
1. Introduction
2. Description of Basic Algorithm
3. Apriori Algorithm based on Cost-Sensitive Non-frequent Filter Matrix
3.1 Cost-Sensitive Learning
3.2 Seek k –Frequency Set by Using Non-Frequency Filter Matrix
3.3 Generate Strong Correlation Rules
3.4 Generate Non-Frequent Filter Matrix
4. Data Mining in Cloud Computing
5. Analysis of Improved Apriori Algorithm in Cloud Computing
6. Conclusion
References