Dataset

Upload CSV or Excel (.xlsx) file

Try with a sample dataset

Pick one to explore instantly — no upload needed

Outlier Detection

k-distance scoring

Epsilon (ε) Strategy

Density-Based

Maximum neighborhood distance

Auto epsilon estimation

K-distance quantile

Conservative (50%)75%Inclusive (99%)

Epsilon Tips:

• Small ε: More clusters, more noise (conservative)

• Large ε: Fewer clusters, less noise (permissive)

• Default 75% works for most datasets

Enable ε sweep

Min Samples

Core point threshold (≥ dims + 1 recommended)

Minimum samples5

Guidelines:

• Low (2-3): Sensitive to noise, many small clusters

• Medium (5): Balanced for most datasets

• High (10+): Robust, only large dense clusters

Distance Metric

How to measure point similarity

Euclidean works well for scaled numeric data. Use Cosine for high-dimensional or text-based features.

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

Epsilon (ε)

—

Neighborhood radius

Clusters

—

Points: 0

Core Points

—

Dense regions

Border Points

—

Cluster boundaries

Noise Points

—

Outliers

Distance

—

Neighbor search

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

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…