A virtual reality system for high-dimentional data visualization

It is generally difficult to extract the essence or meaning of complexly distributed multivariate data. The essential information is distributed in the data space. Therefore, we have to perform both local and global analyses of the given data set. It is also possible that the data-space contains important information that is encoded locally. Such information will be difficult to extract without performing local data analysis. Effective data visualization is also very important to support intuitive understanding o f the meaning embedded in the data. In this paper we propose a virtual reality system for high-dimensional data visualization as a tool for intuitive data understanding. Our method is based on data clustering techniques and Principal Component Analysis (PCA), in which data can be recursively clustered to obtain arbitrary degree of locality and then analyzed using PCA. We have also developed a virtual reality system supported by stereo-glass and motion-capture that enables a user to " tour" the 4-dimensional data space. The ability of the proposed system to present data in 4-dimensional space in which each axis is the eigen vector generated by PCA, allows us to intuitively visualize multivariate data without losing too much information. Experiments using a number of real world data show the effectiveness of the proposed system for intuitive understanding of complex data.