|Method||Description||Recommended Uses||How to Use|
|Available in the Patterns tab in Column Details Panel|
|By function||You can apply one or more specific functions to cleanse your data of minor errors in formatting or structure.||Edit column with formula in the Transform Builder.|
|Mix-and-match||You can use combinations of the above methods for more complex use cases.|
Using one of the supported matching algorithms,
|D s product|
Example - Multiple methods of clustering
The following dataset includes some values that could be standardized:
When you standardize using a spelling-based algorithm, the following values are clustered:
After you select the cluster of values at top, you can enter
apple, in the right context panel to replace that cluster of values with a single string.
In the above, the unclustered values are dissimilar in spelling, but in English, they sound the same (homonyms). When you select the Pronunciation-based algorithm, these values are clustered:
When you select the top values clustered by pronunciation, you can enter
pear in the right context panel.
The six source values have been reduced to two final values through two different methods of clustering. See below for more information on the clustering algorithms.
You can apply cluster-based standardization through the Standardize Page. See Standardize Page.
The following algorithms for clustering values are supported.
For comparing similar strings, the following methods can be applied:
The fingerprint method compares values in the column by applying the following steps to the data before comparing and clustering:
NOTE: These steps are applied to an internal representation of the data. Your dataset and recipe are not changed by this comparison. Changes are only applied if you choose to modify the values and add the mapping.
- Remove accents from characters, so that only ASCII characters remain.
- Change all characters to lowercase.
- Remove whitespace.
- Split the string on punctuation, any remaining whitespace, and control characters. Remaining characters are assembled into groups called tokens.
- Sort the tokens and remove any duplicates.
- Join the tokens back together.
- Compare all tokenized values in the column for purposes of clustering.
This method follows the same steps as those listed above, except that tokens are broken up based on a specific (N) number of characters. By default,
|D s product|
Tip: This method can provide higher fidelity matching, although there may be performance impacts on columns with a high number of unique values.
Values are clustered based on a language-independent pronunciation.
This method uses the double metaphone algorithm for string comparison. For more information, see Compare Strings.
|D s advfeature|
Standardize Formatting by Patterns
|Custom Type Method||Description|
You can upload a dictionary file containing the list of accepted values for the custom type.
For more information, see Create Custom Data Types.
A custom data type can be created based on a user-defined regular expression.
For more information, see Create Custom Data Types Using RegEx.