Cluster Similar Values
Fuzzy-group near-duplicate text (typos, casing, spacing) in one column and get a suggested canonical value per cluster. Runs entirely in your browser, no upload.
Cluster similar values
Real-world columns are full of the "same" value written many ways — New York,
new york, New York, Nwe York. This tool fuzzy-clusters the values in
one column so those near-duplicates land in the same group, then proposes a
canonical form (the most frequent original) for each cluster. Feed it a CSV
or a plain list, one value per line. Everything runs in your browser; nothing is
uploaded.
How it works
Each value is compared with a normalized Levenshtein edit-distance ratio scored 0–100. Two values join the same cluster when their similarity is at or above the threshold. Before comparing, values are optionally folded to ignore letter case and extra spacing, so those differences don't count against a match. The most frequent original in each cluster becomes its canonical suggestion.
Worked example
Input (one value per line), threshold 70:
New York
new york
New York
Nwe York
Boston
The four New-York variants (a typo, a case change, a double space) cluster
together with canonical New York, while Boston stays on its own. The
Mapping table then gives you every original → canonical pair to apply back to
your data.
Options
- Column — for a CSV, the header name (with First row is a header on) or a 1-based index. Blank uses the only column, which is what a newline list is.
- Delimiter — comma, tab, semicolon, pipe, or any single character (ignored for a plain list with no separators).
- Threshold (0–100) — higher is stricter:
100merges only values that are identical after normalization; lower merges looser matches.85is a good start; drop toward70to catch more typos, raise it if unrelated values merge. - Ignore case / Ignore extra spacing — fold those differences before comparing (on by default).
- Output — markdown (clusters plus a mapping table), csv (a flat
cluster,original,canonical,countmapping you can join back), or json (structured clusters with per-value counts).
FAQ
Which value becomes the canonical suggestion?
The most frequent original value in the cluster (ties break toward the one seen first). It's kept exactly as it appears in your data — casing and spacing intact — so you can trust it as the "correct" spelling to standardize on.
How do I turn a threshold into "how many typos are allowed"?
The score is (1 − edits ÷ length) × 100, where length is the longer of the two
values. So for an 8-character value, a threshold of 85 tolerates about one edit
(insert, delete, or substitute a character); 75 tolerates about two. Shorter
values need a lower threshold to merge, since a single edit is a bigger fraction.
Does it change my data or just report clusters?
It only reports. You get the clusters and a mapping table — nothing in your input
is rewritten. Use the CSV mapping (original → canonical) to apply the merges in
your spreadsheet or script.
How is this different from removing duplicate rows?
Plain de-duplication only removes rows that match exactly. This finds values that are nearly the same — typos, casing, and spacing differences — which exact matching misses, and suggests a single canonical spelling to standardize them to.
What are the limits?
It clusters one column at a time and compares values by character edits, so it's ideal for names, cities, companies, and tags — not for matching semantically equal but differently-worded phrases. Blank cells are skipped. Greedy clustering compares each value to its cluster's most-frequent seed, so extremely large, noisy columns may group slightly differently than an exhaustive pairwise pass.
Is my data uploaded?
No — it's processed locally with WebAssembly and never leaves your browser.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool cluster-similar-values "New York
new york
New York
Nwe York
Boston"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/cluster-similar-values/?data=New%20York%0Anew%20york%0ANew%20%20York%0ANwe%20York%0ABoston&column=city&delimiter=%2C&header=true&threshold=85&normalize_case=true&normalize_spacing=true&output=markdownMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.