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 according to its high dimensional distances. This procedure is repeated a specified number of epochs or iterations respectively. The distances in the original high dimensional space have to be provided by a column containing distance vectors (see distance matrix calculate node). According to these distances the data is mapped onto the lower dimensional space. In the low dimensional space the Euclidean distance is used to arrange the points.
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Excel Reader (XLS)6 %- Distance Matrix Calculate5 %
- Distance Matrix Reader4 %
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Excel Reader (XLS)1 %Random Forest Learner< 1 %Joiner< 1 %- Partitioning< 1 %
- Missing Value< 1 %
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Excel Reader (XLS)< 1 %- Rowset Filter< 1 %
- Indigo Fingerprint< 1 %
- Java Snippet< 1 %
- MDS (DistMatrix)< 1 %
PCA< 1 %- PCA Compute< 1 %
Row Filter (deprecated)< 1 %- String Replacer< 1 %
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Scorer< 1 %- PCA< 1 %
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Number To String< 1 %Linear Correlation< 1 %Row Splitter (deprecated)< 1 %- Pivot< 1 %
K Nearest Neighbor (Distance Function)< 1 %- R Snippet< 1 %
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- MDS Projection (DistMatrix)< 1 %
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- Numeric Distances< 1 %
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Workflows
(3D) Fragment library design workflowKNIME Hub- 01_ClusteringKNIME Hub
- 01_ClusteringKNIME Hub
- Chemical SpaceKNIME4HTS
- log-analysis_v0.2KNIME Hub
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