원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.8
2016.08
pp.79-88
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper proposes an improved clustering algorithm on the basis of the characteristics of sampling and density. The initial k value and initial center are determined by sampling and density, and parallel improvement is based on the HADOOP platform. Through the experiment, the improved K-Means algorithm has good parallelism.
목차
Abstract
1. Introduction
2. Idea of K-Means Algorithm
2.1. Procedure of the Algorithm
2.2. Shortcomings of the Algorithm
3. Improved K-Means Algorithm Based on Sampling and Density
3.1. Concept
3.2. Parallelized Improvement
4. Experiment Design and Discussion
4.1. Clustering Analysis
4.2. Running Time
4.3. Acceleration Rate
5. Conclusion
References
1. Introduction
2. Idea of K-Means Algorithm
2.1. Procedure of the Algorithm
2.2. Shortcomings of the Algorithm
3. Improved K-Means Algorithm Based on Sampling and Density
3.1. Concept
3.2. Parallelized Improvement
4. Experiment Design and Discussion
4.1. Clustering Analysis
4.2. Running Time
4.3. Acceleration Rate
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보