PP: Pattern Trails: visual analysis of pattern transitions in subspaces

Problem:

1. We can't find patterns in full attribute space, and patterns may only be found in smaller subspaces.

2. Pattern Trails is an interactive visual approach for the exploration of subspaces of multivariate data

Related work:

1. Multivariate data analysis and visualization

parallel coordinate plots; pixel bar charts; Chernoff faces.

2. using dimensionality reduction for visual analysis

MDS: multidimensional scaling;

PCA: principal component analysis;

t-SNE: t-distributed stochastic neighbor embedding;

SOM: self-organizing maps

3. Subspace search and visualization

Methodology

1. Derive interesting subspaces of the multivariate data

Supplementary knowledge:

1. what are subspace patterns and subspace analysis.

2. The main task in understanding such multivariate data is to identify and interpret relevant patterns like dense groups (clusters), outliers, or correlations.

3. Techniques for visually exploring multivariate data:

Parallel Coordinate Plots;

dimensionality reduction降维; to transform the data to a lower-dimensional space but preserving the main structure of the data.

4. Chernoff Faces

5.