This node maps data of a high dimensional space onto a lower (usually 2 or 3) dimensional space. Therefore the Sammons mapping is applied, which iteratively decreases the difference of the distances of high and low dimensional data. Each original data point is represented by a data point of a lower dimension. The Sammons mapping tries to keep the distance information of the high dimensional data by adjusting the low dimensional data points in a certain way. Each low dimensional data point is moved around a bit towards or back from the other points accordant to its high dimensional distances. This procedure is repeated a specified number of epochs or iterations respectively.
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Popular Successors
Linear Correlation5 %CSV Writer3 %- Scatter Plot (Plotly)3 %
- 2D/3D Scatterplot3 %
- X-Partitioner3 %
Scorer3 %- k-Means2 %
- MDS2 %
- Scorer2 %
- Statistics2 %
Excel Writer (XLS)2 %- Feature Selection Loop Start (1:1)2 %
- MDS (DistMatrix)2 %
- MDS Projection2 %
- Column Filter2 %
- Denormalizer2 %
- Sorter2 %
- Scatter Plot (legacy)2 %
- Color Manager2 %
- Shape Manager2 %
- Scatter Plot2 %
- Similarity Search2 %
- Line Chart (JFreeChart)2 %
- Scatter Plot (JavaScript)2 %
- R Snippet2 %
- Normalizer1 %
Column Rename1 %- Line Plot (legacy)1 %
PCA Compute1 %- Distance Matrix Calculate1 %
- 3D Scatter Plot (Plotly)1 %
- Bubble Chart (JFreeChart)1 %
Naive Bayes Learner< 1 %- Hierarchical Clustering< 1 %
- MultiLayerPerceptron Predictor< 1 %
PCA< 1 %- Linear Regression Learner< 1 %
- Correlation Filter< 1 %
- Column Splitter< 1 %
- Numeric Outliers< 1 %
- Interactive Pie chart (legacy)< 1 %
- Scatter Matrix (legacy)< 1 %
PMML Predictor< 1 %- R View (Table)< 1 %
- Matlab Plot< 1 %
- Matlab Snippet< 1 %
Python Plot< 1 %- Open in R< 1 %
- R Plot< 1 %
- ARIMA Learner< 1 %
- Upload (legacy)< 1 %
- ARFF Writer< 1 %
Database Writer< 1 %- Fuzzy Rule Learner< 1 %
- Cluster Assigner< 1 %
- Hierarchical Clustering (DistMatrix)< 1 %
k-Means< 1 %- DBSCAN< 1 %
- Decision Tree Learner< 1 %
- Decision Tree Predictor< 1 %
- K Nearest Neighbor< 1 %
- MDS Projection (DistMatrix)< 1 %
- RProp MLP Learner< 1 %
- OPTICS Cluster Assigner< 1 %
- OPTICS Cluster Compute< 1 %
PCA Apply< 1 %- Logistic Regression Predictor< 1 %
- Regression Predictor< 1 %
Numeric Scorer< 1 %- SVM Predictor< 1 %
- Tree Ensemble Learner (Regression)< 1 %
- Random Forest Learner< 1 %
- Concatenate< 1 %
- Auto-Binner< 1 %
Number To String< 1 %- Number to String< 1 %
Column Appender< 1 %- One to Many< 1 %
- Linear Correlation< 1 %
- Nominal Value Row Filter< 1 %
Row Splitter (deprecated)< 1 %Row Filter (deprecated)< 1 %- GroupBy< 1 %
Joiner< 1 %- Joiner< 1 %
- Partitioning< 1 %
- Rank< 1 %
- RowID< 1 %
- Bland-Altman Plot< 1 %
Linear Discriminant Analysis< 1 %- Histogram (legacy)< 1 %
- Box Plot (legacy)< 1 %
- Parallel Coordinates (legacy)< 1 %
Color Appender< 1 %- Interactive Table (legacy)< 1 %
- Numeric Distances< 1 %
- Matrix Distance< 1 %
- k-Medoids< 1 %
- Stacked Area Chart (JavaScript)< 1 %
- Boosting Learner Loop End< 1 %
- External Tool< 1 %
- Scatter Plot (JFreeChart)< 1 %
- Tag Cloud< 1 %
- t-SNE (L. Jonsson)< 1 %
- Weka Predictor (3.7)< 1 %
- Column Selection (legacy)< 1 %
- Heatmap (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
- Line Plot (JavaScript)< 1 %
- Data to Report (BIRT)< 1 %
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Workflows
00_Topic_Modeling_with_Verified_ComponentsKNIME Hub- 00_Topic_Modeling_with_Verified_ComponentsKNIME Hub
- 03_Topic_Explorer_ExampleKNIME Hub
- 06_Topic_Modeling_LDA_Data_AppKNIME Hub
- 07_Topic_Modeling_STM_Data_AppKNIME Hub
- Show all 11 workflows
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