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Four Techniques for Outlier Detection

PreprocessingOutlierData cleaningZ-scoreDBSCAN
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Jun 20, 2018 9:40 AM
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This workflow accesses a sample of data from the airline dataset and detects outlier airports based on the average arrival delay in them. The techniques applied are numeric outlier, z-score, DBSCAN and isolation forest. The outlier airports detected by each of these techniques are visualized on a map of US using the KNIME OSM integration.

External resources

  • Four Techniques for Outlier Detection
  • Airline data collected and published by DOT Bureau of Transportation Statistics
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Used extensions & nodes

Created with KNIME Analytics Platform version 4.7.2 Note: Not all extensions may be displayed.
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    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.2

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    KNIME Interactive R Statistics IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime
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    KNIME JavaScript ViewsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime
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    KNIME JavasnippetTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime
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    KNIME Math Expression (JEP)Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime
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    KNIME Open Street Map IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime
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    KNIME Python 2 Integration (legacy)

    KNIME AG, Zurich, Switzerland

    Version 4.7.1

    knime
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    KNIME Quick FormsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.2

    knime
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    KNIME Statistics NodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.7.0

    knime

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