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📊 Visual Interpretation Guide for Clustering Models

🎯 Purpose

This guide provides visual tools and interpretation strategies for understanding and communicating the output of clustering models. It complements the foundational and advanced clustering guidebooks by focusing on practical evaluation, validation, and visual storytelling.

🧭 1. Dimensionality Reduction + Cluster Visualization

Tools:

  • PCA (Principal Component Analysis)
  • UMAP (Uniform Manifold Approximation and Projection)
  • t-SNE (t-distributed Stochastic Neighbor Embedding)

Example:

import umap
import matplotlib.pyplot as plt
embedding = umap.UMAP(n_neighbors=15).fit_transform(X_scaled)
plt.scatter(embedding[:,0], embedding[:,1], c=cluster_labels, cmap="Spectral")

Interpretation:

  • Clusters should show tight groupings
  • Overlapping clusters may indicate poor separability or soft boundaries

📉 2. Silhouette Plot

Purpose:

Show cohesion vs separation of clusters for each observation.

from sklearn.metrics import silhouette_samples, silhouette_score
samples = silhouette_samples(X_scaled, cluster_labels)

Use When:

  • Evaluating internal quality of clusters
  • Comparing different cluster counts

Interpretation:

  • Values close to 1: good cohesion
  • Near 0: overlapping clusters
  • Negative: possible misassignment

🔍 3. Heatmaps of Centroids or Group Means

Purpose:

Compare average feature values across clusters.

Example:

import seaborn as sns
sns.heatmap(cluster_centroids.T, cmap="coolwarm", xticklabels=cluster_ids)

Use When:

  • Clusters are interpretable via feature profiles
  • Useful for segment labeling or business context

📈 4. Cluster Size Distribution

Purpose:

Check whether clusters are imbalanced or dominated by outliers.

import pandas as pd
pd.Series(cluster_labels).value_counts().plot(kind='bar')

Use When:

  • DBSCAN or HDBSCAN includes noise (label = -1)
  • K-Means shows large variance in group size

📊 5. Pairwise Feature Plots (Colored by Cluster)

Purpose:

Reveal internal structure using top features

import seaborn as sns
sns.pairplot(dataframe_with_labels, hue="cluster")

Use When:

  • Working with 2–6 features
  • Clusters may be defined by interactions

🧬 6. Parallel Coordinates / Radial Charts

Purpose:

Compare relative feature values per cluster visually.

from pandas.plotting import parallel_coordinates
parallel_coordinates(summary_df, class_column='cluster')

Use When:

  • Feature scales are comparable
  • Goal is to explain cluster differences

🚦 7. Cluster Label Overlay on Ground Truth (if available)

Purpose:

Compare predicted clusters vs known groupings.

Tool What it shows
Confusion Matrix Cluster ↔ true label alignment
Adjusted Rand Index Score for matching groupings
Colored scatterplot Visual match/mismatch

📌 8. Visual Evaluation Matrix

Goal Recommended Visuals
Inspect shape of clusters PCA/UMAP/t-SNE + color by cluster
Assess quality per point Silhouette plot
Explain cluster profiles Heatmaps, radial plots, parallel coords
Show cluster size Bar plots, pie charts
Compare to labels Confusion matrix, label overlays

📅 TODO

  • [ ] Add example plot gallery
  • [ ] Add UMAP + Silhouette combo plot
  • [ ] Add overlay diagnostic template
  • [ ] Include multi-resolution clustering visual comparison