원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.8 No.1
2015.02
pp.33-40
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
How to have a better mining and use of information in the cloud computing environment constitutes the direction of current research in the field of cloud computing; this paper introduces support vector machine (SVM) concept in the cloud computing data mining, introduces a penalty factor in the SVM, and improves SVM data mining algorithms. The constructed Map / Reduce model by the concept of featured multi-tree conducted the validation of the model. Simulation results show that data mining methods of this model have effectively improved the accuracy and the time of information mining, hence have some practical significance.
목차
Abstract
1. Introduction
2. Related Knowledge
2.1 Support Vector Machines
2.2 Data Mining in the Cloud Computing Environment
2.3 Map / Reduce Model
3. SVM in Cloud Computing Model Environment
3.1 Support Vector Machines with the Introduction of Penalty Factor
3.2 Introduction of Distributed Support Vector Machines in Cloud Computing Model
4. Mining Algorithms Introduced Support Vector Machine based on Map / Reduce Model
4.1 Algorithm Steps Description
4.2 Algorithm Implementation
4.3 Algorithms Example
5. Experimental Simulation
6. Conclusion
References
1. Introduction
2. Related Knowledge
2.1 Support Vector Machines
2.2 Data Mining in the Cloud Computing Environment
2.3 Map / Reduce Model
3. SVM in Cloud Computing Model Environment
3.1 Support Vector Machines with the Introduction of Penalty Factor
3.2 Introduction of Distributed Support Vector Machines in Cloud Computing Model
4. Mining Algorithms Introduced Support Vector Machine based on Map / Reduce Model
4.1 Algorithm Steps Description
4.2 Algorithm Implementation
4.3 Algorithms Example
5. Experimental Simulation
6. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보