Table.AddFuzzyClusterColumn

Table

Adds a column containing representative cluster values based on fuzzy matching of an existing column.

Syntax

Table.AddFuzzyClusterColumn(table as table, columnName as text, newColumnName as text, optional options as nullable record) as table

Parameters

Name	Type	Required	Description
`table`	`table`	Yes	The table to process.
`columnName`	`text`	Yes	The name of the column to cluster.
`newColumnName`	`text`	Yes	The name for the new column containing cluster representative values.
`options`	`record`	No	Optional record with fuzzy options: Threshold, IgnoreCase, IgnoreSpace, NumberOfMatches, TransformationTable.

Return Value

table — The original table with a new column containing the canonical cluster representative for each row.

Remarks

Table.AddFuzzyClusterColumn assigns each row to a cluster based on approximate string similarity of the specified column, then adds a new column containing the representative (canonical) value for that cluster. All variants that fuzzy-match each other receive the same representative value.

Unlike Table.FuzzyGroup, this function preserves all original rows and simply annotates them with a cluster label — it does not aggregate. This makes it useful for previewing cluster assignments before deciding how to proceed.

Examples

Example 1: Add a cluster column for company names

Table.AddFuzzyClusterColumn(
    #table({"Company"}, {{"Microsoft"}, {"Micosoft"}, {"MICROSOFT"}, {"Google"}, {"Gogle"}}),
    "Company",
    "ClusterCompany"
)

Result

	Company	ClusterCompany
1	Microsoft	Microsoft
2	Micosoft	Microsoft
3	MICROSOFT	Microsoft
4	Google	Google
5	Gogle	Google

Example 2: Apply a lower threshold for more aggressive clustering

Table.AddFuzzyClusterColumn(
    #table({"City"}, {{"New York"}, {"New-York"}, {"Newyork"}, {"NY"}}),
    "City",
    "CanonicalCity",
    [Threshold = 0.6]
)