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").
Options
Popular Successors
Loop End (2 ports)7 %- OSM Map View5 %
Row Filter (deprecated)5 %- Hierarchical Clustering4 %
Numeric Scorer4 %- Color Manager3 %
- Pivot2 %
- Shuffle2 %
Column Rename2 %- Rule-based Row Filter2 %
Joiner2 %Variable to Table Column1 %- Statistics1 %
- Scatter Matrix (legacy)1 %
Excel Writer (XLS)1 %- ROC Curve (JavaScript)1 %
- Loop End1 %
- Scorer1 %
Scorer1 %- Denormalizer1 %
- Scatter Plot (legacy)1 %
- Scatter Plot (JFreeChart)1 %
- Scatter Plot (JavaScript)1 %
- Variable to Table Column1 %
Loop End1 %Generic Loop Start1 %- Entropy Scorer1 %
- Concatenate1 %
- Line Plot (legacy)1 %
- Line Chart (JFreeChart)1 %
- CSV Writer< 1 %
- Feature Selection Loop End< 1 %
- Loop End (Column Append)< 1 %
- Silhouette Coefficient< 1 %
- Numeric Scorer< 1 %
- Column Appender< 1 %
- Column Filter< 1 %
- Shape Manager< 1 %
- ROC Curve (legacy)< 1 %
- Interactive Table (legacy)< 1 %
- Scorer (JavaScript)< 1 %
- Parameter Optimization Loop End< 1 %
- Data to Report (BIRT)< 1 %
Table Row to Variable< 1 %- k-Means< 1 %
- Column Merger< 1 %
- Partitioning< 1 %
- Math Formula< 1 %
- Entropy Uncertainty Scorer< 1 %
CSV Writer< 1 %- X-Aggregator< 1 %
- Cluster Assigner< 1 %
- Logistic Regression Learner< 1 %
- HiLite Row Splitter< 1 %
- Missing Value Column Filter< 1 %
- GroupBy< 1 %
- Numeric Outliers< 1 %
- Pie chart (legacy)< 1 %
- Boosting Learner Loop End< 1 %
- OSM Map to Image< 1 %
- Line Plot (JavaScript)< 1 %
Table Writer< 1 %- PCA< 1 %
- Bootstrap Sampling< 1 %
Cell Replacer< 1 %Number To String< 1 %Column Appender< 1 %- Nominal Value Row Filter< 1 %
- Table Manipulator< 1 %
- Normalizer< 1 %
- Numeric Distances< 1 %
- Similarity Search< 1 %
XLS Writer< 1 %- RDKit Add Conformers< 1 %
- Density Scorer< 1 %
- Copy/Move Files (legacy)< 1 %
Variable to Table Column< 1 %Table Row to Variable< 1 %- Table Row to Variable< 1 %
- Table Writer< 1 %
- Feature Selection Filter< 1 %
Variable Loop End< 1 %- PNN Learner (DDA)< 1 %
- Fuzzy c-Means< 1 %
- DBSCAN< 1 %
- Decision Tree Learner< 1 %
- Decision Tree Predictor< 1 %
K Nearest Neighbor< 1 %- K Nearest Neighbor< 1 %
- MultiLayerPerceptron Predictor< 1 %
- RProp MLP Learner< 1 %
- Linear Regression Learner< 1 %
- SMOTE< 1 %
Concatenate (Optional in)< 1 %- Column Aggregator< 1 %
- Column Resorter< 1 %
- One to Many< 1 %
- CAIM Binner< 1 %
- Interactive HiLite Collector< 1 %
Row Splitter (deprecated)< 1 %- Row Filter (Labs)< 1 %
- Joiner< 1 %
- Missing Value< 1 %
- Top k Row Filter< 1 %
- Data Explorer< 1 %
- Extract Table Dimension< 1 %
- Enrichment Plotter (legacy)< 1 %
- Histogram (legacy)< 1 %
- Interactive Histogram (legacy)< 1 %
- Box Plot (legacy)< 1 %
- Parallel Coordinates (legacy)< 1 %
- Save Workflow< 1 %
- TimeDelay Loop End< 1 %
- Matrix Distance< 1 %
- k-Medoids< 1 %
- Bar Chart (JavaScript)< 1 %
- Voting Loop End< 1 %
Excel Sheet Appender (XLS)< 1 %Open TIBCO Spotfire< 1 %- Category to Class< 1 %
- Table View (JavaScript)< 1 %
Simple Table View (JavaScript)< 1 %- JSON Schema Validator< 1 %
Python Learner< 1 %Binary Scorer< 1 %- Information Gain Calculator< 1 %
Views
- 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].
Workflows
01_Loop_over_a_set_of_parameter_for_k_meansKNIME Hub- 02_Mass_Learning_Event_Prediction_MLlib_to_PMMLKNIME Hub
- 02_Mass_Learning_Event_Prediction_MLlib_to_PMMLKNIME Hub
- 02_Mass_Learning_Event_Prediction_MLlib_to_PMMLKNIME Hub
- 04_Parameter_Optimization_in_SparkKNIME Hub
- Show all 23 workflows
Links
- No links available
Developers
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