北京大学高维数据可视化 - 图片集
Dimension Reconstruction for Visual Exploration of Subspace Clusters in High-dimensional Data
In the projection, users can define a new dimension (like RD1 and RD2), where subspace clusters can be separated. Then, he can join the new dimensions in an original subspace (Subspace 3) to maintain the separation of data clusters.
Dimension Projection-Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data
In the dimension projection matrix (1), each cell is either a data projection (top right corner) or a dimension projection (bottom left corner) of a subspace. Rows and columns represent dimensions that constitute the subspaces.
The dimension projection tree: users can brush a group of data items (left) or dimensions (right) to create a child node containing the corresponding subspace.
Visualization Assembly Line
MLMD: Multi-Layered Visualization for Multi-Dimensional Data
Multi-Dimensional Transfer Function Design based on Flexible Dimension Projection Embedded in Parallel Coordinates
Multi-Dimensional Transfer Function Design based on Flexible Dimension Projection Embedded in Parallel Coordinates
Interactive Local Clustering Operations In Parallel Coordinates
Scattering Points in Parallel Coordinates
Splatting the Lines in Parallel Coordinates
Visual Clustering in Parallel Coordinates