원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.10
2016.10
pp.241-250
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Affinity Propagation (AP) is a new clustering method to cluster data set efficiently. In this paper, a Distributed method of Local Affinity Propagation (DLAP) is proposed to solve hardware bottleneck and time-consuming problem. DLAP refines AP by reducing the calculating data scale in each iteration and keeps a high quality clustering result. The method is implemented on Apache Spark distributed computation framework. Depending on high iteration efficiency on Spark, the method has an impressive result in time complexity. Experiments are conducted on two-dimensional data to show that the time cost of LAP on single machine is better than the two methods, FSAP and FAP, meanwhile the result of DLAP on Spark is better than that on Hadoop.
목차
Abstract
1. Introduction
2. Related Work
2.1. Affinity Propagation
2.2. Dataset Scale Reduction
2.3. Parallel Realization
3. Distributed Local Affinity Propagation Method
3.1. Local Dataset Reduction
3.2. Parallel Realization on Spark
4. Experiments
5. Conclusions
References
1. Introduction
2. Related Work
2.1. Affinity Propagation
2.2. Dataset Scale Reduction
2.3. Parallel Realization
3. Distributed Local Affinity Propagation Method
3.1. Local Dataset Reduction
3.2. Parallel Realization on Spark
4. Experiments
5. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보