This node normalizes the values of all (numeric) columns. In the dialog, you can choose the columns you want to work on. The following normalization methods are available in the dialog:
Options
Popular Successors
- Partitioning17 %
- k-Means4 %
- Denormalizer (PMML)4 %
Number To String3 %CSV Writer2 %PCA2 %- PMML Writer2 %
Excel Writer (XLS)2 %PCA Apply< 1 %PMML Predictor< 1 %- Normalizer Apply (PMML)< 1 %
Python Script (1⇒1)< 1 %- CSV Writer< 1 %
- Random Forest Learner< 1 %
- Python Script (legacy)< 1 %
- Naive Bayes Predictor< 1 %
- Hierarchical Clustering< 1 %
- RProp MLP Learner< 1 %
- Logistic Regression Learner< 1 %
- Scatter Plot (legacy)< 1 %
- R View (Table)< 1 %
Table Writer< 1 %- Fuzzy c-Means< 1 %
Logistic Regression Learner< 1 %- Category to Number< 1 %
- GroupBy< 1 %
Naive Bayes Learner< 1 %- PMML Transformation Appender< 1 %
- Missing Value Column Filter< 1 %
- Statistics< 1 %
- Interactive Table (legacy)< 1 %
- Table to H2O< 1 %
- Hierarchical Cluster Assigner (local)< 1 %
- DBSCAN< 1 %
- Random Forest Learner (Regression)< 1 %
- Random Forest Predictor< 1 %
- One to Many< 1 %
- Normalizer< 1 %
- Normalizer (PMML)< 1 %
Number To String (PMML)< 1 %Column Rename< 1 %- Numeric Distances< 1 %
- Scatter Plot (JavaScript)< 1 %
- Hierarchical Clustering (DistMatrix)< 1 %
- Numeric Scorer< 1 %
- Gradient Boosted Trees Learner (Regression)< 1 %
- PMML Model Appender< 1 %
- Domain Calculator< 1 %
- Column Filter< 1 %
- Number to String (PMML)< 1 %
- Data Explorer< 1 %
- Distance Matrix Calculate< 1 %
- k-Medoids< 1 %
- DL4J Feedforward Predictor (Regression) (legacy)< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- X-Partitioner< 1 %
- Naive Bayes Learner< 1 %
- Cluster Assigner< 1 %
- Decision Tree Learner< 1 %
- Decision Tree Predictor< 1 %
- K Nearest Neighbor< 1 %
- MultiLayerPerceptron Predictor< 1 %
RProp MLP Learner< 1 %- Linear Regression Learner< 1 %
- Logistic Regression Predictor< 1 %
Regression Predictor< 1 %- Regression Predictor< 1 %
- SVM Learner< 1 %
- SVM Predictor< 1 %
- PCA Apply< 1 %
- PCA Compute< 1 %
- PCA< 1 %
- Simple Regression Tree Learner< 1 %
- Auto-Binner< 1 %
- Number to String< 1 %
String To Number< 1 %- String to Number< 1 %
- Linear Correlation< 1 %
- Correlation Filter< 1 %
Row Filter (deprecated)< 1 %Joiner< 1 %- Joiner< 1 %
- Normalizer (Apply)< 1 %
- Many to One (PMML)< 1 %
- Missing Value< 1 %
- Sorter< 1 %
- Rule Engine< 1 %
- Numeric Outliers< 1 %
- Histogram (legacy)< 1 %
- Color Manager< 1 %
- PMML to SQL< 1 %
- Boosting Learner Loop Start< 1 %
PMML Ensemble Predictor< 1 %- PMML to Cell< 1 %
- Math Formula< 1 %
Excel Writer (XLS)< 1 %- Excel Writer< 1 %
- Table View (JavaScript)< 1 %
- Line Plot (JavaScript)< 1 %
- Reference Row Filter< 1 %
- Column Filter (PMML)< 1 %
- String to Number (PMML)< 1 %
- Column Splitter< 1 %
- String Manipulation< 1 %
- Value Counter< 1 %
- Rule-based Row Filter< 1 %
- One-way ANOVA< 1 %
- Crosstab< 1 %
- Interactive Pie chart (legacy)< 1 %
- Box Plot (legacy)< 1 %
Color Appender< 1 %XGBoost Tree Ensemble Learner< 1 %- Copy/Move Files (legacy)< 1 %
- Java Snippet< 1 %
Backward Feature Elimination Start (1:1)< 1 %K Nearest Neighbor< 1 %Scorer< 1 %Gradient Boosted Trees Learner< 1 %- Reference Column Filter< 1 %
- Nominal Value Row Filter< 1 %
- Shuffle< 1 %
- Conditional Box Plot (legacy)< 1 %
- PMML Compiling Predictor< 1 %
- Fingerprint Bayesian Learner< 1 %
- Keras Network Learner< 1 %
- Table to PMML Ensemble< 1 %
- Scatter Plot (JFreeChart)< 1 %
- Java Snippet (simple)< 1 %
- Weka Cluster Assigner (3.7)< 1 %
- JPMML Classifier< 1 %
- Heatmap (JavaScript)< 1 %
- Benchmark Start< 1 %
Model Writer< 1 %- X-Aggregator< 1 %
- PNN Learner (DDA)< 1 %
- PNN Predictor< 1 %
k-Means< 1 %- Decision Tree to Image< 1 %
- Gradient Boosted Trees Predictor< 1 %
Cell Replacer< 1 %- Lag Column< 1 %
- Denormalizer< 1 %
- One to Many (PMML)< 1 %
String To Number (PMML)< 1 %- Table Transposer< 1 %
- Ruleset Editor< 1 %
- Ruleset to Table< 1 %
- Linear Discriminant Analysis< 1 %
- Cache< 1 %
- Pie chart (legacy)< 1 %
- Scatter Matrix (legacy)< 1 %
- Mahalanobis Distance< 1 %
- Similarity Search< 1 %
- Bar Chart (JavaScript)< 1 %
- PMML Predictor< 1 %
Excel Sheet Appender (XLS)< 1 %Table R-View< 1 %- Weka Predictor (3.7)< 1 %
- Column Filter (legacy)< 1 %
- Decision Tree View (JavaScript)< 1 %
- Python Learner (legacy)< 1 %
- R Predictor< 1 %
- String to Date&Time< 1 %
- PMML Compiler< 1 %
- Outlier Removal< 1 %
- Plate Heatmap Viewer< 1 %
- R Snippet< 1 %
- 2D/3D Scatterplot< 1 %
- Create Collection Column< 1 %
Database Writer< 1 %- Table Writer< 1 %
PMML Writer< 1 %Backward Feature Elimination Filter< 1 %- Feature Selection Filter< 1 %
Loop End< 1 %Counting Loop Start< 1 %Interval Loop Start< 1 %- Chunk Loop Start< 1 %
- Column List Loop Start< 1 %
- Group Loop Start< 1 %
Naive Bayes Predictor< 1 %- Fuzzy Rule Learner< 1 %
- Silhouette Coefficient< 1 %
- Hierarchical Cluster Assigner< 1 %
- Hierarchical Cluster View< 1 %
Decision Tree Learner< 1 %- MDS< 1 %
- OPTICS Cluster Assigner< 1 %
PCA Compute< 1 %PCA Inversion< 1 %- Polynomial Regression Learner< 1 %
- Scorer< 1 %
- SMOTE< 1 %
- SOTA Learner< 1 %
- Gradient Boosted Trees Learner< 1 %
Gradient Boosted Trees Predictor< 1 %PMML Gradient Boosted Trees Predictor< 1 %- Tree Ensemble Learner (Regression)< 1 %
Tree Ensemble Predictor< 1 %- Tree Ensemble Predictor< 1 %
Random Forest Learner< 1 %Random Forest Predictor< 1 %- Random Forest Predictor (Regression)< 1 %
- PMML Simple Regression Tree Predictor< 1 %
- Simple Regression Tree Predictor< 1 %
- PMML Validation Data Appender< 1 %
- Concatenate< 1 %
- Auto-Binner (Apply)< 1 %
- Numeric Binner< 1 %
- Binner (Dictionary)< 1 %
- Create Bit Vector< 1 %
- Case Converter< 1 %
- Column Comparator< 1 %
- Category to Number (Apply)< 1 %
- Number to Category (Apply)< 1 %
- Column Aggregator< 1 %
Column Appender< 1 %- Column Appender< 1 %
- Column Rename (Regex)< 1 %
- Column Resorter< 1 %
- Constant Value Column< 1 %
Linear Correlation< 1 %- Cross Joiner< 1 %
- Double to Integer< 1 %
- Equal Size Sampling< 1 %
- Extract Missing Value Cause< 1 %
- Reference Column Splitter< 1 %
- Filter Apply Row Splitter< 1 %
- HiLite Row Splitter< 1 %
Row Splitter (deprecated)< 1 %- Low Variance Filter< 1 %
- Numeric Binner (PMML)< 1 %
- Missing Value (Apply)< 1 %
- RowID< 1 %
- Numeric Row Splitter< 1 %
- Row Sampling< 1 %
- Rule-based Row Splitter< 1 %
- Ruleset Predictor< 1 %
- Rank Correlation< 1 %
- Independent groups t-test< 1 %
- End IF< 1 %
- Extract Table Dimension< 1 %
- Interactive Histogram (legacy)< 1 %
- Lift Chart (legacy)< 1 %
- Line Plot (legacy)< 1 %
- Shape Manager< 1 %
- ROC Curve (legacy)< 1 %
- Table to Spark< 1 %
- Capture Workflow End< 1 %
- SDF Writer< 1 %
- Matrix Distance< 1 %
DL Network Executor< 1 %- 3D Scatter Plot (Plotly)< 1 %
- Line Plot (Plotly)< 1 %
- Parallel Coordinates Plot (JavaScript)< 1 %
- Pie/Donut Chart (JavaScript)< 1 %
- Model to Cell< 1 %
- Cell to PMML< 1 %
- MoSS< 1 %
- DL4J Feedforward Learner (Classification) (legacy)< 1 %
- DL4J Feedforward Learner (Pretraining) (legacy)< 1 %
- Math Formula (Multi Column)< 1 %
- GroupBy Bar Chart (JFreeChart)< 1 %
- HeatMap (JFreeChart)< 1 %
- Histogram Chart (JFreeChart)< 1 %
- Java Snippet Row Filter< 1 %
- Radar Plot Appender< 1 %
- Frequency Filter< 1 %
- Category to Class< 1 %
- String to Term< 1 %
- JPMML Cluster Assigner< 1 %
- Value Filter Definition (legacy)< 1 %
- Tag Cloud (JavaScript)< 1 %
- JSON Row Combiner< 1 %
JSON Writer< 1 %- Image Normalizer< 1 %
- Object Inserter< 1 %
- Multiobjective Score Computation< 1 %
- Parameter Optimization Loop Start< 1 %
Python Script (1⇒1)< 1 %- Python Predictor (legacy)< 1 %
- R Learner< 1 %
- Table Difference Checker< 1 %
- Date&Time Difference< 1 %
- Date&Time-based Row Filter< 1 %
- Extract Time Window (legacy)< 1 %
- Moving Aggregator< 1 %
- XGBoost Predictor (Regression)< 1 %
- Column to XML< 1 %
- Discretise< 1 %
Views
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Workflows
!Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub- 0_install_AI_extensionsKNIME Hub
- 01_AnalyticsKNIME Hub
- 01_AnalyticsKNIME Hub
- 01_AutoML_Component_via_Interactive_ViewsKNIME Hub
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Developers
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