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Running dbscan density-based clustering analysis...
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Analyze another fileDiscovers 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
Multivariate numeric data
Minimum 30 rows · Best with 100-10000 rows
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.
2D PCA projection with cluster assignments and noise points
Sorted k-nearest neighbor distances for eps parameter guidance
Number of observations per cluster including noise
Mean feature values per cluster
Detailed cluster statistics and quality metrics
Selected parameters and quality metrics (silhouette score)
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Correlation — Just need correlations, not clusters
Need more power? Pca — Too many dimensions — reduce first, then cluster
Similar: Kmeans
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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