원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.7 No.3
2014.06
pp.41-48
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
For the lack of "density-based spatial clustering with noise" (DBSCAN) algorithm in dealing with large data sets, MapReduce programming model is proposed to achieve the clustering of DBSCAN. Map functions to complete the data analysis, and get clustering rules in different data objects; Then Reduce functions merge these clustering rules to get a final result. Experimental results show: the DBSCAN of MapReduce running on the cloud computing platform Hadoop has good speedup and scalability.
목차
Abstract
1. Introduction
2. MapReduce Programming Model
3. DBSCAN Clustering Algorithm
4. DBSCAN Algorithm based on MapReduce
4.1. Feasibility Analysis
4.2. The Realization of DBSCAN Algorithm based on MapReduce
5. Analysis of Experimental Results
6. Conclusion
Acknowledgements
References
1. Introduction
2. MapReduce Programming Model
3. DBSCAN Clustering Algorithm
4. DBSCAN Algorithm based on MapReduce
4.1. Feasibility Analysis
4.2. The Realization of DBSCAN Algorithm based on MapReduce
5. Analysis of Experimental Results
6. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보