원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.2
2014.04
pp.131-142
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The goal of data mining is to discover hidden useful information in large databases. Mining frequent patterns from transaction databases is an important problem in data mining. As the database size increases, the computation time and required memory also increase. Base on this, we use the MapReduce programming mode which has parallel processing ability to analysis the large-scale network. All the experiments were taken under hadoop, deployed on a cluster which consists of commodity servers. Through empirical evaluations in various simulation conditions, the proposed algorithms are shown to deliver excellent performance with respect to scalability and execution time.
목차
Abstract
1. Introduction
2. Fast Newman Parallel Algorithm
2.1. The Newman Algorithm with Modularity
2.2. Newman Parallel Algorithm and Modularity
3. The Comparison Algorithm with this Article
3.1. Partitioning Around Medoids
3.2. Clustering Large Applications
4. The Simulation and Conclusion
References
1. Introduction
2. Fast Newman Parallel Algorithm
2.1. The Newman Algorithm with Modularity
2.2. Newman Parallel Algorithm and Modularity
3. The Comparison Algorithm with this Article
3.1. Partitioning Around Medoids
3.2. Clustering Large Applications
4. The Simulation and Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보