🧩 Clustering Studio

Compare K-Means, DBSCAN, and Hierarchical clustering on 2D datasets. Adjust parameters, add noise, and understand when each method works best.

Visualization Elbow/Silhouette Controls Guide
Dataset & Clusters
Tip: Click on the canvas to add a point to the selected class (used only for coloring reference; clustering ignores labels).
# Clusters
—
Silhouette
—
Noise
—
Elbow & Silhouette
Elbow (Within-Cluster Sum of Squares)
Silhouette by K (K-Means)
Elbow suggests a good K when the curve bends; Silhouette closer to 1 is better separation.
📦 Dataset
⚙️ Algorithm
K (clusters)3
📊 Metrics
Clusters found: —
Noise points: —
Silhouette score: —
About Clustering & How to Read This
K-Means
  • Partitions data into K spherical clusters by minimizing within-cluster variance
  • Works well for compact, similarly sized clusters
  • Use the Elbow and Silhouette plots to pick K
DBSCAN
  • Density-based; finds arbitrarily shaped clusters and marks outliers as noise
  • Parameters: eps (neighborhood radius), minPts (minimum neighbors)
  • Great for clusters with noise; no need to set K
Hierarchical (Agglomerative)
  • Builds a tree of merges using a linkage metric (single/complete/average)
  • Cut into K clusters; interpretable merge process
Interpreting the visuals
  • Regions: Background tint shows predicted cluster areas
  • Silhouette: Closer to 1 means better separation
  • Noise: Count of outliers (DBSCAN)

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About This Tool & Methodology

This studio implements common clustering algorithms (e.g., k‑means, hierarchical, DBSCAN) and visualizes clusters, centroids, and evaluation scores (silhouette where applicable). Data can be synthetic or user‑provided.

Learning Outcomes

  • Compare clustering methods and hyperparameters (k, linkage, eps/minPts).
  • Interpret silhouette scores and cluster separability.
  • Understand sensitivity to scaling, initialization, and outliers.

Authorship & Review

  • Author: 8gwifi.org engineering team
  • Reviewed by: Anish Nath
  • Last updated: 2025-11-19

Trust & Privacy

  • All computations run locally in your browser.
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