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

Guo-jun MA; Shui-bo WANG; Qing-qi PEI; Yang ZHAN

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

您当前的位置：

首页 >

文章列表页 >

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

Papers | 更新时间：2024-06-03

- Research on parallel coordinate visualization technology based on principal component analysis and K-means clustering
- Chinese Journal of Network and Information Security Vol. 3, Issue 8, Pages: 18-27(2017)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2017.00189
  CLC： TP301
- Online First：2017-08，
  
  Published：15 August 2017
- 稿件说明：
移动端阅览
Guo-jun MA, Shui-bo WANG, Qing-qi PEI, et al. 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.
DOI：

Guo-jun MA, Shui-bo WANG, Qing-qi PEI, et al. 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. DOI： 10.11959/j.issn.2096-109x.2017.00189.

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。

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.

关键词

Keywords

references

REICHMAN O J , JONES M B , SCHILDHAUER M P . Challenges and Opportunities of open data in ecology [J ] . Science , 2011 , 331 ( 6018 ): 703 - 705 .

怀进鹏 . 大数据是国家战略资源 [J ] . 中国经济和信息化 , 2013 ( 8 ): 49 - 50 .

HUAI J P . Big Data:national strategic resource [J ] . China Economy＆ Information , 2013 ( 8 ): 49 - 50 .

高芳 . 平行坐标可视化技术的度量模型研究 [D ] . 北京:北京交通大学 , 2009 .

GAO F . Research on measurement model of parallel coordinates visualization technique [D ] . Beijing:Beijing Jiaotong University , 2009 .

胡俊 , 黄厚宽 , 任东怀 . 一种基于图标技术的可视化方法与应用 [J ] . 北京交通大学学报 , 2006 , 30 ( 5 ): 10 - 14 .

HU J , HUANG H K , REN D H . An icon_based visualization approach and its applications [J ] . Journal of Beijing Jiaotong University , 2006 , 30 ( 5 ): 10 - 14 .

ITOH T , YAMAGUCHI Y , IKEHATA Y , et al . Hierarchical data visualization using a fast rectangle-packing algorithm [J ] . IEEE Transactions on Visualization ＆ Computer Graphics , 2004 , 10 ( 3 ): 302 - 313 .

CHEN P , JIAO L , LIU F , et al . Dimensionality reduction of hyper-spectral imagery using sparse graph learning [J ] . IEEE Journal of Selected Topics in Applied Earth Observations ＆ Remote Sensing , 2016 , PP ( 99 ): 1 - 17 .

INSELBERG A . Parallel coordinates [J ] . Encyclopedia of Database Systems , 2009 , 43 ( 4 ): 199 - 233 .

解坤 , 张俊芳 . 基于 KMO-Bartlett 典型风速选取的 PCA-WNN短期风速预测 [J ] . 发电设备 , 2017 , 31 ( 2 ): 12 - 36 .

XIE K , ZHANG J F . Short-term wind speed forecasting using PCA-WNN based on KMO-bartlett typical wind speed selection [J ] . Power Equipment , 2017 , 31 ( 2 ): 12 - 36 .

WANG J . Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks [J ] . Neuro Computing , 2015 , 156 ( C ): 68 - 78 .

HASHEMINEJAD S M H , JALILI S . CCIC:clustering analysis classes to identify software components [J ] . Information ＆ Software Technology , 2015 ,( 57 ): 329 - 351 .

段明秀 . 层次聚类算法的研究与应用 [D ] . 长沙:中南大学 , 2009 .

DUAN M X . Research and application of hierarchical clustering algorithm [D ] . ChangSha:Central South University , 2009 .

JAIN A K . Data clustering:50 years beyond K-means [J ] . Pattern Recognition Letters , 2010 , 31 ( 8 ): 651 - 666 .

Views

1502

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Review of malware detection and classification visualization techniques

Behavior authentication of Web users based on machine learning

Visualization analysis and design of DDoS attack

Successive memory image analysis method for malicious codes

Related Author

Jinwei WANG

Zhengjia CHEN

Xue XIE

Xiangyang LUO

Bin MA

Zenan WU

Liqin TIAN

Zhigang WANG

Related Institution

Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology

Department of Computer, Nanjing University of Information Science and Technology

State Key Laboratory of Mathematical Engineering and Advanced Computing

University of Science and Technology of China

China Aerospace Academy of Systems Science and Engineering

⁰