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DBSCAN Clustering In Minutes

Upload numeric data, discover natural clusters without specifying k. Free.

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Running dbscan density-based clustering analysis...

Running dbscan density-based clustering analysis...

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Sample Output

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How it works

Discovers clusters of arbitrary shape without specifying how many clusters to find. Identifies dense regions as clusters and marks isolated points as noise — good for finding natural groupings in messy data.

Use this when you have numeric data and want to find clusters without pre-specifying k, especially if clusters aren't spherical.

If you know how many clusters you want, use K-Means. If you need interpretable segments with labels, use RFM Segmentation.

Built for: Data scientist, analyst, researcher

Typical data source: Numeric dataset with 2+ variables where natural groupings may exist

analyticsresearchecommerceoperations

What data do you need?

Multivariate numeric data

feature_1 (numeric) feature_2 (numeric)
3.5 12.0
7.2 8.5
1.8 15.3

Minimum 30 rows · Best with 100-10000 rows

What's in the report?

Density-based spatial clustering that automatically discovers clusters of arbitrary shape without pre-specifying k. Identifies noise/outlier points and handles non-spherical cluster geometries. Uses eps (neighborhood radius) and minPts (minimum points) parameters.

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Cluster Scatter Plot (PCA)

2D PCA projection with cluster assignments and noise points

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K-Distance Plot

Sorted k-nearest neighbor distances for eps parameter guidance

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Cluster Size Distribution

Number of observations per cluster including noise

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Cluster Profiles

Mean feature values per cluster

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Cluster Statistics

Detailed cluster statistics and quality metrics

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DBSCAN Parameters

Selected parameters and quality metrics (silhouette score)

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

Related tools

Need something simpler? Correlation — Just need correlations, not clusters

Need more power? Pca — Too many dimensions — reduce first, then cluster

Similar: Kmeans

Questions?

See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.

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