This use case demonstrates how the Approximate String Matcher node can be used to detect potential errors or rare entries by matching the least frequent values against the most frequent ones in the same dataset.
Using approximate string matching (e.g., Levenshtein distance), we can distinguish:
Likely typos — low-frequency entries that closely resemble high-frequency ones
Rare but valid values — dissimilar entries that are truly unique
Correct entries — high-frequency values, often assumed correct
This makes it ideal for:
Detecting entry errors in location, product, or customer data
Auto-flagging suspicious or rare strings for review
Improving data quality in human-entered datasets