Correlation Visualization of Time-Varying Patterns for Multi-Variable Data

Correlation analysis is one of the most important tasks in the field of visualization research and data mining. This paper proposes a novel dissimilarity-preserving cluster algorithm that characterizes not only the time-varying patterns but also the spatial positions to summary the correlation connection in multi-variable and time-varying data sets. A temporal multi-variable structure is defined to express temporal information of a voxel in multi-dimensional space. Furthermore, a method based on structural similarity index measurement is proposed to compute the difference of time-varying pattern. In order to further explore some abnormal phenomena, spatial similarity is embedded as spatial distance metric by building the kernel density estimate for the neighborhood of each voxel. To verify the effectiveness of the method, the voxels are classified based on the time-varying similarity and spatial distance. Moreover, the combinations of two metrics are rebalanced to be suitable for the different datasets. The approach proposed in this paper is used on both synthetic and real-world data sets to demonstrate its usefulness and effectiveness.