Earticle

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

키워드

저자

• Shuguang Wang [ Jilin Communications Polytechnic, Changchun 130012, china ]
• Chao Jiang [ Jilin Communications Polytechnic, Changchun 130012, china ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605