Entropy Scorer (deprecated)

This Node Is Deprecated — This node is kept for backwards-compatibility, but the usage in new workflows is no longer recommended. The documentation below might contain more information.

Scorer for clustering results given a reference clustering. Connect the table containing the reference clustering to the first input port (the table should contain a column with the cluster IDs) and the table with the clustering results to the second input port (it should also contain a column with some cluster IDs). Select the respective columns in both tables from the dialog. After successful execution, the view will show entropy values (the smaller the better) and some quality value (in [0,1] - with 1 being the best possible value, as used in Fuzzy Clustering in Parallel Universes, section 6: "Experimental results").


Reference column
Column containing the reference clustering. This column is provided by the first input table.
Clustering column
Column containing the cluster IDs to evaluate. This column is provided by the second input table.

Input Ports

Table containing reference clustering.
Table containing clustering (to score).

Output Ports

This node has no output ports

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Statistics View
Simple statistics on the clustering such as number of clusters being found, number of objects in clusters, number of reference clusters, and total number of objects. Further statistics include:
  • Entropy: The accumulated entropy of all identified clusters, weighted by the relative cluster size. The entropy is not normalized and may be greater than 1.
  • Quality: The quality value according to the formula referenced above. It is the sum of the weighted qualities of the individual clusters, whereby the quality of a single cluster is calculated as (1 - normalized_entropy). The domain of the quality value is [0,1].
The table at the bottom of the view provides statistics on cluster size, cluster entropy and normalized cluster entropy. The entropy of a clusters is based on the reference clustering (provided at the first input port) and the normalized entropy is this value scaled to an interval [0, 1]. More precisely, it is the entropy divided by log2(number of different clusters in the reference set).


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