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

网络与信息安全学报 ›› 2017, Vol. 3 ›› Issue (8): 18-27.doi: 10.11959/j.issn.2096-109x.2017.00189

基于主成分分析和K-means聚类的平行坐标可视化技术研究

马国峻^1,²(),王水波²,裴庆祺²,詹阳²

¹ 西安文理学院信息工程学院，陕西西安 710065
² 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071

修回日期:2017-08-04 出版日期:2017-08-01 发布日期:2017-12-26
作者简介:马国峻（1978-），男，安徽无为人，西安文理学院讲师，主要研究方向为数字内容保护、智能移动应用开发、区块链应用与安全。|王水波（1990-），男，湖北黄梅人，西安电子科技大学硕士生，主要研究方向为Web前端。|裴庆祺（1975-），男，广西玉林人，西安电子科技大学教授、博士生导师，主要研究方向为信任管理、无线网络安全、区块链安全。|詹阳（1977-），男，陕西杨凌人，西安电子科技大学讲师，主要研究方向为信息安全、区块链应用。
基金资助:
国家自然科学基金资助项目(61373170)

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)

摘要/Abstract

摘要：

为了解决多维数据的维数过高、数据量过大带来的平行坐标可视化图形线条密集交叠以及数据规律特征不易获取的问题，提出基于主成分分析和K-means聚类的平行坐标（PCAKP,principal component analysis and k-means clustering parallel coordinate）可视化方法。该方法首先对多维数据采用主成分分析方法进行降维处理，其次对降维后的数据采用K-means聚类处理，最后对聚类得到的数据采用平行坐标可视化技术进行可视化展示。以统计局网站发布的数据为测试数据，对PCAKP可视化方法进行测试，与传统平行坐标可视化图形进行对比，验证了PCAKP可视化方法的实用性和有效性。

关键词: 数据可视化, 平行坐标可视化, 主成分分析, K-means聚类

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

中图分类号:

TP301

马国峻,王水波,裴庆祺,詹阳. 基于主成分分析和K-means聚类的平行坐标可视化技术研究[J]. 网络与信息安全学报, 2017, 3(8): 18-27.

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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基于主成分分析和K-means聚类的平行坐标可视化技术研究

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

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