Multivariate and Clustering
Start here when your question is exploratory and involves many variables, grouped profiles, reduced-space views, or candidate cluster structure rather than single distributions or simple two-variable relationships.
Overview
- Use this branch when your question is exploratory and involves many variables, grouped profiles, reduced-space views, or candidate cluster structure.
- If you mainly need a single-variable distribution or grouped comparison view, use Distribution and Group Comparison; if you mainly need a two-variable relationship or agreement view, use Association and Regression.
- This branch covers exploratory multivariate and clustering displays, not proof of separation, confirmation of latent structure, or automatic discovery results.
- Some pages are also narrower than their titles suggest, so page-level support boundaries matter.
Child pages
- Heatmap
- Parallel Coordinates
- PCA Biplot
- K-means Clustering Plot
- Hierarchical Clustering Heatmap
- Confidence Ellipse
- Convex Hull
Start here
- Start with Heatmap when you want to scan many values for matrix-like pattern candidates.
- Use Parallel Coordinates when each observation has several variables and the profile across axes matters.
- Use PCA Biplot when you need a cautious reduced-space view of multivariable structure, not proof that groups are truly separated.
- Move to K-means Clustering Plot or Hierarchical Clustering Heatmap only when clustering itself is part of the exploratory question.
- Use Confidence Ellipse or Convex Hull when you want grouped scatter overlays, not standalone proof-like summaries.
Related
- Related links are registered in frontmatter and rendered below this stub.