k-Means (distance)

This component runs K-means algorithm and outputs the Euclidean distance between every point and the clusters' centroids.

In the configuration dialog, you can select whether to calculate the distance to every cluster's centroid or only to the one each points belongs to.

The clustering algorithm uses the Euclidean distance on the selected attributes. The data is not normalized by the node (if required, you should consider to use the "Normalizer" as a preprocessing step).

This component is free to use and modify.
Author: Andrea De Mauro, aboutbigdata.net

Options

Columns selection (all numeric columns will be used for clustering)

Select columns to be kept (only and all numeric columns will be used for clustering).

Number of clusters

Number of clusters to be created.

Computes distance between each point and:

Select wheter you would like to see the distance between every point and every cluster or only the cluster which the point belongs to.

Nodes

• Column Filter4 ×
• Chunk Loop Start2 ×
• Column Appender2 ×
• Column Rename2 ×
• Java Edit Variable (simple)2 ×
• Loop End (Column Append)2 ×
• Numeric Distances2 ×
• RowID2 ×
• Similarity Search2 ×
• CASE Switch Data (End)1 ×
• CASE Switch Data (Start)1 ×
• Column Aggregator1 ×
• Column Filter Configuration1 ×
• Component Input1 ×
• Component Output1 ×
• Integer Configuration1 ×
• Java Snippet (simple)1 ×
• Single Selection Configuration1 ×
• Sorter1 ×
• String To Number1 ×
• k-Means1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Distance Matrix
• FeatureKNIME Javasnippet
• FeatureKNIME Quick Forms

Links

• No links available