원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.2
2015.02
pp.301-310
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
SnIClustering Algorithm is put forward to deal with the large number of intermediate values when processing MapReduce. SnIClustering Algorithm picks up a few representative data through cluster sampling, and then retains the useful data through filtration according to the distribution characteristics. By doing so, intermediate values of MapReduce can be reduced sharply, saving time and easing network load. The last step is to cluster the selected data and samples. Experimental results show that SnIClustering is suitable to process large-scale data, since it can both process large-scale data within a short time and maintain fine clustering effect.
목차
Abstract
1. Introduction
2. Relevant Works
3. SnIclustering Algorithms
3.1. Algorithmic Thinking
3.2. Algorithm Description
4. Experimental Evaluation
4.1. Data-set and Parameter
4.2. Experimental Results and Analysis
5. Conclusions
Acknowledgements
Referneces
1. Introduction
2. Relevant Works
3. SnIclustering Algorithms
3.1. Algorithmic Thinking
3.2. Algorithm Description
4. Experimental Evaluation
4.1. Data-set and Parameter
4.2. Experimental Results and Analysis
5. Conclusions
Acknowledgements
Referneces
저자정보
참고문헌
자료제공 : 네이버학술정보