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 Predecessors
Scorer13 %- k-Means10 %
- Scorer4 %
Numeric Scorer4 %- Cluster Assigner4 %
- Decision Tree Predictor3 %
CSV Reader2 %- Hierarchical Clustering2 %
- Scatter Plot (JavaScript)2 %
- DBSCAN2 %
- Parameter Optimization Loop End2 %
- Numeric Scorer1 %
- Color Manager1 %
Excel Reader (XLS)1 %- Partitioning1 %
Naive Bayes Predictor< 1 %K Nearest Neighbor< 1 %- File Reader< 1 %
- Column Splitter< 1 %
- Fuzzy c-Means< 1 %
- Regression Predictor< 1 %
- Column Filter< 1 %
- CSV Reader< 1 %
- Entropy Scorer< 1 %
- Normalizer< 1 %
- Silhouette Coefficient< 1 %
Loop End< 1 %Joiner< 1 %- ARFF Reader< 1 %
Cell Replacer< 1 %- GroupBy< 1 %
- Shuffle< 1 %
- Logistic Regression Predictor< 1 %
Column Appender< 1 %- Round Double< 1 %
- Rule Engine< 1 %
- Data Generator< 1 %
- K Nearest Neighbor< 1 %
- Linear Regression Learner< 1 %
- Gradient Boosted Trees Predictor< 1 %
- Simple Regression Tree Predictor< 1 %
- Scorer (JavaScript)< 1 %
- X-Aggregator< 1 %
Random Forest Predictor< 1 %- Random Forest Predictor< 1 %
- Denormalizer< 1 %
- Value Counter< 1 %
- ROC Curve (legacy)< 1 %
- Math Formula< 1 %
- XGBoost Predictor< 1 %
- Excel Reader< 1 %
- MultiLayerPerceptron Predictor< 1 %
- OPTICS Cluster Assigner< 1 %
- Nominal Value Row Filter< 1 %
- Naive Bayes Learner< 1 %
- Naive Bayes Predictor< 1 %
- Number to String< 1 %
- K Nearest Neighbor (Distance Function)< 1 %
- PMML Predictor< 1 %
- File Reader (Complex Format)< 1 %
- Joiner< 1 %
- DL4J Feedforward Predictor (Classification) (legacy)< 1 %
Naive Bayes Learner< 1 %PCA< 1 %- Column Expressions< 1 %
XLS Reader< 1 %Excel Reader (XLS)< 1 %- PCA< 1 %
- Shape Manager< 1 %
- Column List Loop Start< 1 %
- Logistic Regression Learner< 1 %
Regression Predictor< 1 %Random Forest Predictor< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
- Random Forest Predictor (Regression)< 1 %
Number To String< 1 %- Row Filter< 1 %
- Rule-based Row Filter< 1 %
- Statistics< 1 %
- k-Medoids< 1 %
- PNN Predictor< 1 %
- Hierarchical Cluster Assigner< 1 %
- SVM Predictor< 1 %
- Category to Number< 1 %
- Domain Calculator< 1 %
- Pivot< 1 %
- Numeric Outliers< 1 %
- ROC Curve (JavaScript)< 1 %
- Keras Network Executor< 1 %
Table Reader< 1 %Interval Loop Start< 1 %- Hierarchical Cluster Assigner (local)< 1 %
- HiLite Row Splitter< 1 %
- Weka Predictor (3.7)< 1 %
Gradient Boosted Trees Predictor< 1 %- Row Splitter< 1 %
- String Replacer< 1 %
- Parallel Chunk End< 1 %
Tree Ensemble Predictor< 1 %- Missing Value< 1 %
Column Rename< 1 %- Weka Cluster Assigner (3.7)< 1 %
Table Row To Variable Loop Start< 1 %- Table Creator< 1 %
Random Forest Learner< 1 %- Auto-Binner< 1 %
- Normalizer (Apply)< 1 %
- String to Number (PMML)< 1 %
- Table Transposer< 1 %
Excel Reader (XLS)< 1 %- Topic Extractor (Parallel LDA)< 1 %
- Line Plot (JavaScript)< 1 %
- String to Number< 1 %
Recursive Loop Start< 1 %k-Means< 1 %- SMOTE< 1 %
- Column Rename (Regex)< 1 %
- Row Sampling< 1 %
- Box Plot (legacy)< 1 %
- Interactive Table (legacy)< 1 %
- Association Rule Learner (Borgelt)< 1 %
Counting Loop Start< 1 %- Fuzzy Rule Learner< 1 %
Tree Ensemble Predictor< 1 %Linear Correlation< 1 %- Ruleset Predictor< 1 %
- H2O to Table< 1 %
- Item Set Finder (Borgelt)< 1 %
- LIBSVMPredictor< 1 %
- Term Document Entropy< 1 %
- Python Source (legacy)< 1 %
- Feature Selection Loop End< 1 %
- Compiled Model Predictor< 1 %
- Split Collection Column< 1 %
- PNGs to Binary Objects< 1 %
Variable to Table Column< 1 %File Reader< 1 %- Feature Selection Loop Start (1:1)< 1 %
Generic Loop Start< 1 %- Fuzzy Rule Predictor< 1 %
Random Forest Learner< 1 %- Random Forest Learner< 1 %
- Concatenate< 1 %
- Column Comparator< 1 %
- Column Resorter< 1 %
Number To String (PMML)< 1 %- Sorter< 1 %
- Crosstab< 1 %
PMML Predictor< 1 %- R Predictor< 1 %
- Copy/Move Files (legacy)< 1 %
- Strings to Binary Objects< 1 %
- Inject Variables (Data)< 1 %
- X-Partitioner< 1 %
PCA Apply< 1 %- Polynomial Regression Learner< 1 %
- SOTA Predictor< 1 %
Tree Ensemble Predictor (Regression)< 1 %Gradient Boosted Trees Predictor< 1 %- PMML Gradient Boosted Trees Predictor< 1 %
- Tree Ensemble Learner (Regression)< 1 %
- Tree Ensemble Predictor< 1 %
- Tree Ensemble Predictor (Regression)< 1 %
- PMML Simple Regression Tree Predictor< 1 %
- Numeric Binner< 1 %
- Create Bit Vector< 1 %
- Bootstrap Sampling< 1 %
String To Number< 1 %- Linear Correlation< 1 %
- Correlation Filter< 1 %
- Duplicate Row Filter< 1 %
- Equal Size Sampling< 1 %
- Interactive HiLite Collector< 1 %
- Reference Row Filter< 1 %
- Low Variance Filter< 1 %
- RowID< 1 %
String Replace (Dictionary)< 1 %- Decision Tree to Ruleset< 1 %
- Ruleset Editor< 1 %
- Ruleset to Table< 1 %
- Rank Correlation< 1 %
- End IF< 1 %
- Enrichment Plotter (legacy)< 1 %
- Spark Scorer< 1 %
- Random Number Assigner< 1 %
- Distance Matrix Calculate< 1 %
K Nearest Neighbor (Distance Function)< 1 %- 3D Scatter Plot (Plotly)< 1 %
Read Excel Sheet Names (XLS)< 1 %- Bag Of Words Creator< 1 %
- Weka Predictor (3.6) (legacy)< 1 %
- Decision Tree View (JavaScript)< 1 %
- Table Editor (JavaScript)< 1 %
Python Script (1⇒2)< 1 %- XGBoost Predictor (Regression)< 1 %
- Information Gain Calculator< 1 %
- Random Forest Learner (Regression)< 1 %
- Constant Value Column< 1 %
- Constant Value Column Filter< 1 %
- Row Splitter (Labs)< 1 %
- Denormalizer (PMML)< 1 %
- Independent groups t-test< 1 %
- IF Switch< 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 18 workflows
Links
- No links available
Developers
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