Hub
Pricing About
WorkflowWorkflow

Frequency-Aware Anomaly Detection

Text cleaningError categorizationApproximate string matchingData cleaning
exorbyte-Team profile image
Draft Latest edits on 
Aug 8, 2025 12:19 PM
Drag & drop
Like
Download workflow
Workflow preview

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

External resources

  • exorbyte GmbH
Loading deploymentsLoading ad hoc jobs

Used extensions & nodes

Created with KNIME Analytics Platform version 5.4.5
  • Go to item
    exorbyte matchmaker toolboxTrusted extension

    exorbyte GmbH

    Version 1.0.2

    exorbyte-Team profile image
    exorbyte-Team
  • Go to item
    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.4.4

    knime
  • Go to item
    KNIME JavasnippetTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.4.3

    knime

Legal

By using or downloading the workflow, you agree to our terms and conditions.

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • Courses + Certification
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more about KNIME Business Hub
© 2025 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Data Processing Agreement
  • Credits