Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

Distance-to-centroid scoring

Number of Clusters

Bottom-Up

Target clusters or use distance threshold

Use distance threshold

Number of clusters3

Compare cluster counts

Linkage Method

How to measure cluster distances

Ward linkage uses Euclidean distance (required by algorithm).

Ward works best for compact spherical clusters. Complete and average are more robust to noise.

Distance Metric

How to measure point similarity

LOCKEDWard linkage requires Euclidean distance (metric disabled).

Hierarchy Options

Dendrogram and tree settings

Compute full tree

Preprocessing

Recommended transformations

Enable preprocessing

Advanced settings

Projection

Choose embedding for visualization

Projection method

Parameter Guide

Click to expand

Performance Metrics

Clusters

—

Hierarchical groups

Silhouette

—

Weak separation

Calinski-Harabasz

—

Inter-cluster variance

Davies-Bouldin

—

Good separation

Linkage

—

Variance minimization

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.

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Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

Distance-to-centroid scoring

Number of Clusters

Bottom-Up

Target clusters or use distance threshold

Use distance threshold

Number of clusters3

Compare cluster counts

Linkage Method

How to measure cluster distances

Ward linkage uses Euclidean distance (required by algorithm).

Ward works best for compact spherical clusters. Complete and average are more robust to noise.

Distance Metric

How to measure point similarity

LOCKEDWard linkage requires Euclidean distance (metric disabled).

Hierarchy Options

Dendrogram and tree settings

Compute full tree

Preprocessing

Recommended transformations

Enable preprocessing

Advanced settings

Projection

Choose embedding for visualization

Projection method

Parameter Guide

Click to expand

Performance Metrics

Clusters

—

Hierarchical groups

Silhouette

—

Weak separation

Calinski-Harabasz

—

Inter-cluster variance

Davies-Bouldin

—

Good separation

Linkage

—

Variance minimization

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.

Agglomerative Hierarchical Clustering — Interactive Analysis