What data can I cluster?

Paste a comma-separated CSV or choose a local UTF-8 file up to 5 MB. Supply 2 to 2,000 data rows, up to 64 source columns and 1 to 20 selected numeric columns. Deselect identifiers and other text columns. Missing, malformed, overflowing and underflowing selected numbers produce a row and column error instead of being deleted or replaced.

How are the k-means centers initialized?

The integer seed selects the first source row. Each remaining center is the row farthest from its nearest existing center, with earlier rows breaking ties. Assignments use Euclidean distance and each update uses arithmetic means. The run stops when assignments stabilize or after 100 complete iterations. A fixed dataset, column order, settings and seed reproduce the same run.

What happens if a k-means cluster is empty?

The empty cluster receives the farthest point from its current assigned center whose cluster still contains more than one row. Equal distances choose the earliest source row. Every repair identifies the row, donor and receiving cluster in the trace and CSV. Duplicate points remain separate rows.

When should I use z-score scaling?

Scaling changes the distance model by subtracting each selected column mean and dividing by its sample standard deviation, using n minus 1. It can make differently sized units comparable for exploration, but it does not establish the correct grouping. Constant columns cannot be z-scored. The scatter plot retains original units, while scaled centroids and SSE are labelled as z-score quantities.

How do the hierarchical linkage methods differ?

Single linkage uses the nearest pair between clusters, complete linkage uses the farthest pair, and average linkage uses the mean of every cross-cluster pair distance. The complete tree supports up to 500 rows. Choose 2 to 20 groups before running. Equal-distance merges use stable node order, so a chosen number of groups can split merges at a tied dendrogram height.

What is included in the exports?

The CSV contains every selected original value, final assignment, metric and original centroid, scaling parameter, complete k-means iteration assignment and empty-cluster repair, plus every hierarchical merge when applicable. Project JSON includes the source CSV, settings, projection and results. Restoring a project validates its inputs and requires a new run; saved result values are not trusted. SVG exports contain the current projection or complete dendrogram.

Are my data uploaded or saved automatically?

No. CSV parsing and clustering run locally in a cancellable worker, and this tool does not persist your data. Editing or cancelling terminates pending work and invalidates stale results. Files are created only when you request an export.

Clustering Lab

Cluster your numeric CSV in a local workspace with deterministic k-means and single, complete or average hierarchical linkage. Choose dimensions and sample z-score scaling, inspect the assignments and centroids, then export the full analysis as CSV or a reusable project. Every completed k-means iteration and hierarchical merge remains available for inspection.

Data, distances, visible decisionsNumeric CSV → groups → complete traces

01 · Supplied data

Paste comma-separated valuesOr select a local UTF-8 CSVFirst row contains column names

Up to 2,000 rows, 20 selected numeric columns, 64 source columns and 5 MB. Missing and nonfinite selected cells are errors, never zero-filled or deleted.

02 · Numeric columns and method

1. X2. Y

4 rows · 2 selected columns

AlgorithmClusters / hierarchy cutSeed, unsigned 32-bit integer

Sample z-score scaling, using n − 1

03 · Two-axis projection

Original example preview

Horizontal axisVertical axis

Cluster scatter projection of X and Y; 4 points, 2 clusters. Full coordinates and assignments follow. — Points retain original units. White crosses are centroids. The selected columns, including those outside this projection, determine grouping.

Within-cluster SSE: 4
Clusters: 2

SSE uses original units across every selected dimension. Different scales and selected columns change distances.

Original four-point example shown. Paste your data to explore its groups.

Rows: 4

Dimensions: 2

Complete iterations / merges: 2

Scaling: None

Restore a Clustering Lab project JSON

Inputs are validated before replacing current work. Saved results are recomputed only after Run clustering.

Four-point fixture(0,0), (0,2), (10,0), (10,2), k = 2
Centroids (0,1), (10,1); SSE 4
Single linkage1D points 0, 2, 10
Merge distances 2, then 8

Descriptive unsupervised grouping, not classification, person assessment or a claim of objectively correct clusters. Euclidean distance; deterministic original implementations. Hierarchical mode capped at500rows; no demographic inference.

Common questions

What data can I cluster?: Paste a comma-separated CSV or choose a local UTF-8 file up to 5 MB. Supply 2 to 2,000 data rows, up to 64 source columns and 1 to 20 selected numeric columns. Deselect identifiers and other text columns. Missing, malformed, overflowing and underflowing selected numbers produce a row and column error instead of being deleted or replaced.
How are the k-means centers initialized?: The integer seed selects the first source row. Each remaining center is the row farthest from its nearest existing center, with earlier rows breaking ties. Assignments use Euclidean distance and each update uses arithmetic means. The run stops when assignments stabilize or after 100 complete iterations. A fixed dataset, column order, settings and seed reproduce the same run.
What happens if a k-means cluster is empty?: The empty cluster receives the farthest point from its current assigned center whose cluster still contains more than one row. Equal distances choose the earliest source row. Every repair identifies the row, donor and receiving cluster in the trace and CSV. Duplicate points remain separate rows.
When should I use z-score scaling?: Scaling changes the distance model by subtracting each selected column mean and dividing by its sample standard deviation, using n minus 1. It can make differently sized units comparable for exploration, but it does not establish the correct grouping. Constant columns cannot be z-scored. The scatter plot retains original units, while scaled centroids and SSE are labelled as z-score quantities.
How do the hierarchical linkage methods differ?: Single linkage uses the nearest pair between clusters, complete linkage uses the farthest pair, and average linkage uses the mean of every cross-cluster pair distance. The complete tree supports up to 500 rows. Choose 2 to 20 groups before running. Equal-distance merges use stable node order, so a chosen number of groups can split merges at a tied dendrogram height.
What is included in the exports?: The CSV contains every selected original value, final assignment, metric and original centroid, scaling parameter, complete k-means iteration assignment and empty-cluster repair, plus every hierarchical merge when applicable. Project JSON includes the source CSV, settings, projection and results. Restoring a project validates its inputs and requires a new run; saved result values are not trusted. SVG exports contain the current projection or complete dendrogram.
Are my data uploaded or saved automatically?: No. CSV parsing and clustering run locally in a cancellable worker, and this tool does not persist your data. Editing or cancelling terminates pending work and invalidates stale results. Files are created only when you request an export.