Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

Distance-to-medoid scoring

Clusters (k)

Partitioning

Auto-select or pin medoid count

Auto k-selection

Number of clusters: 3

Medoids are actual samples—ideal for exemplar discovery.

Max Iterations

Medoid Insights

Optional per-point diagnostics

Include per-sample metrics

Adds silhouette and distance-to-medoid columns to deep-dive tables. Useful for exemplar analysis.

Solver Configuration

Tune medoid updates and distance metric

Update Method

Alternate is scalable; PAM performs exhaustive swaps for maximum accuracy.

Initialization

Heuristic is stable; k-medoids++ improves spread for complex datasets.

Distance Metric

Choose the geometry that matches your domain similarity measure.

Preprocessing

Data transformation pipeline

Apply preprocessing

Advanced Settings

Visualization

2D projection method

Projection

Parameter Guide

Click to expand

Performance Metrics

Clusters (k)

User-defined

Silhouette Score

0.000

Poor

Inertia (WCSS)

Compactness

Iterations

Convergence

Duration

0ms

Compute Time

Interactive Visualization

Cluster Visualization

Interactive scatter plot with cluster centroids

Run training to render the scatter plot

Advanced Analysis

Deep Dive Analysis

Advanced metrics and interpretation tools

Silhouette Analysis

Per-sample quality scores grouped by cluster

Mean

0.000

Best

0.000

Worst

0.000

Enable per-sample metrics during training to generate cluster-level silhouette coefficients.

Recent Jobs

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

Loading…

Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

Distance-to-medoid scoring

Clusters (k)

Partitioning

Auto-select or pin medoid count

Auto k-selection

Number of clusters: 3

Medoids are actual samples—ideal for exemplar discovery.

Max Iterations

Medoid Insights

Optional per-point diagnostics

Include per-sample metrics

Adds silhouette and distance-to-medoid columns to deep-dive tables. Useful for exemplar analysis.

Solver Configuration

Tune medoid updates and distance metric

Update Method

Alternate is scalable; PAM performs exhaustive swaps for maximum accuracy.

Initialization

Heuristic is stable; k-medoids++ improves spread for complex datasets.

Distance Metric

Choose the geometry that matches your domain similarity measure.

Preprocessing

Data transformation pipeline

Apply preprocessing

Advanced Settings

Visualization

2D projection method

Projection

Parameter Guide

Click to expand

Performance Metrics

Clusters (k)

User-defined

Silhouette Score

0.000

Poor

Inertia (WCSS)

Compactness

Iterations

Convergence

Duration

0ms

Compute Time

Interactive Visualization

Cluster Visualization

Interactive scatter plot with cluster centroids

Run training to render the scatter plot

Advanced Analysis

Deep Dive Analysis

Advanced metrics and interpretation tools

Silhouette Analysis

Per-sample quality scores grouped by cluster

Mean

0.000

Best

0.000

Worst

0.000

Enable per-sample metrics during training to generate cluster-level silhouette coefficients.

Recent Jobs

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

K-Medoids Clustering — Interactive Analysis