원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.8
2016.08
pp.21-30
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Spatial Cluster analysis is another important technique in the field of spatial data mining, especially the K-Means spatial clustering method, which can deal with spatial objects with geographical location and attribute. However, with the development of the information society, the spatial data grows explosively, but the serial algorithm has low computing efficiency and is difficult to process massive spatial data. Aiming at spatial with a double meaning of location and attribute, the paper designed and implemented K-Means spatial clustering parallel algorithm on Hadoop. Using Yahoo Weibo user data is to do clustering analysis. Finally, the visualization of clustering results was implemented by Google Map.
목차
Abstract
1. Introduction
2. K-Means Spatial Clustering Algorithm
2.1. Spatial Clustering
2.2. K-Means Clustering
2.3. K-Means Spatial Clustering
3. Design of Spatial Clustering Algorithm Based on K-Mean Value
3.1. Parallel Analysis of the Algorithm
4. Experiment Design and Discussion
4.1. Data Pre-Treatment
4.2. Similarity Measurement
4.3. Visualization and Analysis of Clustering Results
5. Conclusion
Acknowledgement
References
1. Introduction
2. K-Means Spatial Clustering Algorithm
2.1. Spatial Clustering
2.2. K-Means Clustering
2.3. K-Means Spatial Clustering
3. Design of Spatial Clustering Algorithm Based on K-Mean Value
3.1. Parallel Analysis of the Algorithm
4. Experiment Design and Discussion
4.1. Data Pre-Treatment
4.2. Similarity Measurement
4.3. Visualization and Analysis of Clustering Results
5. Conclusion
Acknowledgement
References
저자정보
참고문헌
자료제공 : 네이버학술정보