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.
Options
Popular Predecessors
File Reader12 %PCA5 %- Distance Matrix Calculate5 %
- Low Variance Filter3 %
Row Filter (deprecated)2 %- k-Medoids2 %
- PCA2 %
Joiner2 %Excel Reader (XLS)2 %- k-Means2 %
- MDS2 %
- Column Filter2 %
Row Splitter (deprecated)2 %- GroupBy2 %
- Normalizer2 %
- CSV Reader2 %
- Split Collection Column2 %
- Color Manager2 %
Word Vector Apply (legacy)2 %- SMOTE2 %
- List Box Configuration2 %
- RDKit From Molecule2 %
Table Reader1 %- PNN Predictor1 %
- Expand Bit Vector< 1 %
String To Number< 1 %Excel Reader (XLS)< 1 %- Extract System Properties< 1 %
- MDS Projection< 1 %
- RDKit Descriptor Calculation< 1 %
CSV Reader< 1 %- Concatenate< 1 %
- Bootstrap Sampling< 1 %
Number To String< 1 %- Partitioning< 1 %
- RowID< 1 %
- Column Splitter< 1 %
- Molecule Type Cast< 1 %
- Similarity Search< 1 %
- Molecular Properties< 1 %
Database Reader< 1 %- Java Snippet< 1 %
- Hierarchical Clustering< 1 %
PCA Apply< 1 %- String to Number< 1 %
- Correlation Filter< 1 %
- String Manipulation< 1 %
- Data Generator< 1 %
- Shape Manager< 1 %
Excel Reader (XLS)< 1 %- Table Reader< 1 %
- Linear Correlation< 1 %
- Prediction Fusion< 1 %
- Weka Cluster Assigner (3.7)< 1 %
- Double Configuration< 1 %
- Modify Date< 1 %
- Create Collection Column< 1 %
- String to URI< 1 %
Loop End (2 ports)< 1 %Loop End< 1 %- X-Partitioner< 1 %
- Polynomial Regression Learner< 1 %
- Create Bit Vector< 1 %
- Category to Number< 1 %
Column Appender< 1 %- One to Many< 1 %
- Nominal Value Row Filter< 1 %
- Missing Value< 1 %
- Normalizer (PMML)< 1 %
Column Rename< 1 %- Row Sampling< 1 %
- Table Transposer< 1 %
- Category to Class< 1 %
- Document Vector< 1 %
- Strings to Binary Objects< 1 %
- ARFF Reader< 1 %
Naive Bayes Predictor< 1 %- Fuzzy c-Means< 1 %
K Nearest Neighbor< 1 %- MDS (DistMatrix)< 1 %
- MDS Projection (DistMatrix)< 1 %
PCA Compute< 1 %- Linear Regression Learner< 1 %
- Logistic Regression Learner< 1 %
- Distance Matrix Pair Extractor< 1 %
Concatenate (Optional in)< 1 %Linear Correlation< 1 %- Domain Calculator< 1 %
- Double to Integer< 1 %
- Filter Apply< 1 %
- Interactive HiLite Collector< 1 %
- Missing Value Column Filter< 1 %
- Reference Row Filter< 1 %
- Rule-based Row Filter< 1 %
- Extract Table Dimension< 1 %
- Crosstab< 1 %
PMML Predictor< 1 %XLS Reader< 1 %- Document Data Extractor< 1 %
- Weka Predictor (3.7)< 1 %
- Scatter Plot (JavaScript)< 1 %
- Fingerprints< 1 %
- RDKit Fingerprint< 1 %
- File Reader< 1 %
- File Reader (Complex Format)< 1 %
- Logistic Regression Predictor< 1 %
- Rule-based Row Splitter< 1 %
- Statistics< 1 %
- Math Formula (Multi Column)< 1 %
- Line Plot (JavaScript)< 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
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