基于主成分分析和K-means聚类的平行坐标可视化技术研究

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。

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

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.