基于主成分分析和K-means聚类的平行坐标可视化技术研究

doi:10.11959/j.issn.2096-109x.2017.00189

Chinese Journal of Network and Information Security ›› 2017, Vol. 3 ›› Issue (8): 18-27.doi: 10.11959/j.issn.2096-109x.2017.00189

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Research on parallel coordinate visualization technology based on principal component analysis and K-means clustering

Guo-jun MA^1,²(),Shui-bo WANG²,Qing-qi PEI²,Yang ZHAN²

¹ School of Information Engineering,Xi’an University,Xi’an 710065,China
² State Key Laboratory of Integrated Service Networks,Xidian University,Xi’an 710071,China

Revised:2017-08-04 Online:2017-08-01 Published:2017-12-26
Supported by:
The National Natural Science Foundation of China(61373170)

Abstract

Abstract:

In order to solve the problem that parallel coordinate visualization graphic lines are intensive,overlap and rules of data is not easy to be obtained which caused by high dimension and immense amount of multidimensional data.Parallel coordinate visualization method based on principal component analysis and K-means clustering was proposed.In this method,the principal component analysis method was used to reduce the dimensionality of the multidimensional data firstly.Secondly,the data of the dimension reduction was clustered by K-means.Finally,the data of the clustering were visualized by parallel coordinate visualization.The PCAKP visualization method is tested with the data published by the Bureau of Statistics as the test data,and compared with the traditional parallel coordinate visualization graph,the validity and effectiveness of the PCAKP visualization method are verified.

Key words: data visualization, parallel coordinate visualization, principal component analysis, K-means clustering

CLC Number:

TP301

Guo-jun MA,Shui-bo WANG,Qing-qi PEI,Yang ZHAN. Research on parallel coordinate visualization technology based on principal component analysis and K-means clustering[J]. Chinese Journal of Network and Information Security, 2017, 3(8): 18-27.

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