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outlier_​detection_​techniques

Four Techniques for Outlier Detection
Read Data Preprocess Data Outlier Detection - Numeric Outlier Outlier Visualization This workflow detects outliers in the data using the following techniques: numeric outlier, z-score, DBSCAN and isolation forest. Outlier Detection - Z-Score Outlier Detection - DBSCAN Outlier Detection - Isolation Forest Detect outliersGroup by arrival airportRead airlinedataClusteringDistance functionDetect outliersIsolation forestk-valueThreshold zMinPtsEpsilon MapViz Numeric Outliers Preproc Read data DBSCAN Numeric Distances Merge Variables Mark outliers Row Filter Density of delay MISSING PythonScript (1⇒1) Mark outliers Mark outliers Mark outliers MapViz MapViz MapViz DoubleConfiguration DoubleConfiguration IntegerConfiguration DoubleConfiguration Read Data Preprocess Data Outlier Detection - Numeric Outlier Outlier Visualization This workflow detects outliers in the data using the following techniques: numeric outlier, z-score, DBSCAN and isolation forest. Outlier Detection - Z-Score Outlier Detection - DBSCAN Outlier Detection - Isolation Forest Detect outliersGroup by arrival airportRead airlinedataClusteringDistance functionDetect outliersIsolation forestk-valueThreshold zMinPtsEpsilon MapViz Numeric Outliers Preproc Read data DBSCAN Numeric Distances Merge Variables Mark outliers Row Filter Density of delay MISSING PythonScript (1⇒1) Mark outliers Mark outliers Mark outliers MapViz MapViz MapViz DoubleConfiguration DoubleConfiguration IntegerConfiguration DoubleConfiguration

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Created by: Maarit Laukkanen
Created at: 2018-06-20
On NodePit since: 2020-07-29
Last update: 2024-04-26
Created with KNIME version: v4.1.2
Tags: preprocessingoutlierdata cleaningz-scoreDBSCANnumeric outlierisolation forest

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