Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

Distance-to-mode scoring

Bandwidth Strategy

Mode-Seeking

Kernel size estimation and search grid

Auto bandwidth estimation

Bandwidth grid (optional)

Provide comma-separated values to evaluate alongside the estimated bandwidth.

Quantile for auto bandwidth

Broader radius30%Finer radius

Sample size

Sample fraction

Run configuration

Control cluster assignment behaviour

Assign every point

Bin seeding

Minimum bin frequency

Controls how dense a region must be before it spawns a cluster.

Max iterations

Random state

Max features for estimator

Diagnostics

Per-point metrics for deep dives

Include per-sample metrics

Adds distance-to-mode and silhouette scores for each sample.

Preprocessing

Recommended feature transformations

Enable preprocessing

Advanced settings

Projection

Choose embedding for 2D visualization

Projection method

PCA is deterministic and highlights global variance. UMAP captures complex manifolds for exploratory analysis.

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.

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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-mode scoring

Bandwidth Strategy

Mode-Seeking

Kernel size estimation and search grid

Auto bandwidth estimation

Bandwidth grid (optional)

Provide comma-separated values to evaluate alongside the estimated bandwidth.

Quantile for auto bandwidth

Broader radius30%Finer radius

Sample size

Sample fraction

Run configuration

Control cluster assignment behaviour

Assign every point

Bin seeding

Minimum bin frequency

Controls how dense a region must be before it spawns a cluster.

Max iterations

Random state

Max features for estimator

Diagnostics

Per-point metrics for deep dives

Include per-sample metrics

Adds distance-to-mode and silhouette scores for each sample.

Preprocessing

Recommended feature transformations

Enable preprocessing

Advanced settings

Projection

Choose embedding for 2D visualization

Projection method

PCA is deterministic and highlights global variance. UMAP captures complex manifolds for exploratory analysis.

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.

Mean Shift Clustering — Interactive Analysis