The fuzzy c-means algorithm is a well-known unsupervised learning
technique that can be used to reveal the underlying structure of the data.
Fuzzy clustering allows each data point to belong to several clusters, with
a degree of membership to each one.
Make sure that the input data is normalized to obtain better clustering
results.
The list of attributes to use can be set in the second tab of the
dialog.
The first output datatable provides the original datatable with the
cluster memberships to each cluster. The second datatable provides
the values of the cluster prototypes.
Additionally, it is possible to induce a noise cluster, to detect noise
in the dataset, based on the approach from R. N. Dave: 'Characterization
and detection of noise in clustering'.
Options
Popular Successors
Scorer9 %- Color Manager5 %
- Cluster Assigner4 %
- Silhouette Coefficient2 %
- Rule-based Row Filter2 %
- Random Forest Learner2 %
PMML Writer2 %- RProp MLP Learner1 %
Linear Correlation1 %- Interactive Table (legacy)1 %
XLS Writer1 %- Partitioning1 %
- Scorer1 %
- Pivot1 %
- Decision Tree Predictor1 %
- Entropy Scorer< 1 %
- String Replacer< 1 %
Naive Bayes Learner< 1 %Column Appender< 1 %- GroupBy< 1 %
PMML Predictor< 1 %- Denormalizer< 1 %
- 3D Scatter Plot (Plotly)< 1 %
- Fuzzy c-Means< 1 %
- Auto-Binner< 1 %
- Column Aggregator< 1 %
- Value Counter< 1 %
- Box Plot (legacy)< 1 %
- 2D/3D Scatterplot< 1 %
- Fuzzy Rule Learner< 1 %
- Hierarchical Cluster Assigner (local)< 1 %
- PCA Compute< 1 %
- RowID< 1 %
- Column Splitter< 1 %
- One-way ANOVA< 1 %
- Lift Chart (legacy)< 1 %
- Scatter Plot (legacy)< 1 %
- Distance Matrix Calculate< 1 %
- Similarity Search< 1 %
- PMML to Cell< 1 %
- Pie Chart (JFreeChart)< 1 %
- Bubble Chart (JFreeChart)< 1 %
- Rule Viewer (local) (Legacy)< 1 %
- CSV Writer< 1 %
Model Writer< 1 %- Naive Bayes Predictor< 1 %
- Hierarchical Clustering< 1 %
- Hierarchical Cluster View< 1 %
- k-Means< 1 %
- DBSCAN< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Linear Regression Learner< 1 %
- SOTA Learner< 1 %
- SOTA Predictor< 1 %
- PCA< 1 %
- Concatenate< 1 %
- Column Renamer< 1 %
- Column Filter< 1 %
Row Filter (deprecated)< 1 %Joiner< 1 %- Joiner< 1 %
- Denormalizer (PMML)< 1 %
Column Rename< 1 %- Row Sampling< 1 %
- String Manipulation< 1 %
- Unpivot< 1 %
- Rule Engine< 1 %
- Crosstab< 1 %
- Scatter Matrix (legacy)< 1 %
- Shape Manager< 1 %
- K Nearest Neighbor (Distance Function)< 1 %
- Line Plot (Plotly)< 1 %
- Math Formula< 1 %
- Scatter Plot (JFreeChart)< 1 %
- OSM Map View< 1 %
Excel Writer (XLS)< 1 %- Excel Writer< 1 %
- t-SNE (L. Jonsson)< 1 %
- Decision Tree View (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
- Scatter Plot (JavaScript)< 1 %
- Data to Report (BIRT)< 1 %
XGBoost Tree Ensemble Learner (Regression)< 1 %- Java Snippet< 1 %
Naive Bayes Predictor< 1 %- PMML Simple Regression Tree Predictor< 1 %
- k-Medoids< 1 %
- PMML Predictor< 1 %
- GroupBy Bar Chart (JFreeChart)< 1 %
- JPMML Cluster Assigner< 1 %
- Line Plot (JavaScript)< 1 %
- PNN Learner (DDA)< 1 %
Numeric Scorer< 1 %- Create Bit Vector< 1 %
Cell Replacer< 1 %String To Number< 1 %- Column Appender< 1 %
- Column Resorter< 1 %
- Cross Joiner< 1 %
Row Splitter (deprecated)< 1 %- Normalizer< 1 %
- Line Plot (legacy)< 1 %
- Parallel Coordinates (legacy)< 1 %
K Nearest Neighbor (Distance Function)< 1 %- NG Construct&View< 1 %
- Table to H2O< 1 %
- Radar Plot Appender< 1 %
- JPMML Regression Predictor< 1 %
- R View (Table)< 1 %
- Entropy Uncertainty Scorer< 1 %
Variable to Table Column< 1 %- Table to Image< 1 %
- PMML Writer< 1 %
Table Writer< 1 %Loop End (2 ports)< 1 %- X-Partitioner< 1 %
- Naive Bayes Learner< 1 %
- Hierarchical Cluster Assigner< 1 %
K Nearest Neighbor< 1 %PCA Apply< 1 %- Numeric Scorer< 1 %
Gradient Boosted Trees Learner< 1 %- PMML Model Appender< 1 %
- Numeric Binner< 1 %
- Number to String< 1 %
- One to Many< 1 %
- HiLite Row Splitter< 1 %
- Reference Row Filter< 1 %
- Missing Value (Apply)< 1 %
- Missing Value< 1 %
- Rule-based Row Splitter< 1 %
- Interactive Histogram (legacy)< 1 %
- Interactive Pie chart (legacy)< 1 %
Color Appender< 1 %- PMML to SQL< 1 %
- Bar Chart (JavaScript)< 1 %
- Parallel Coordinates Plot (JavaScript)< 1 %
- Boosting Learner Loop End< 1 %
- Bar Chart (JFreeChart)< 1 %
- LIBSVMPredictor< 1 %
Excel Sheet Appender (XLS)< 1 %- Spark Line Appender< 1 %
- JPMML Classifier< 1 %
- Column Selection (legacy)< 1 %
- Tag Cloud (JavaScript)< 1 %
- R Snippet< 1 %
- Type< 1 %
- Plate Heatmap Viewer< 1 %
- Variable to Table Column< 1 %
Image Writer (Table Column)< 1 %- ARFF Writer< 1 %
CSV Writer< 1 %- Model Writer< 1 %
- Feature Selection Filter< 1 %
- Feature Selection Loop Start (2:2)< 1 %
- Loop End (Column Append)< 1 %
Loop End< 1 %- Group Loop Start< 1 %
- X-Aggregator< 1 %
- Fuzzy Rule Predictor< 1 %
- PNN Predictor< 1 %
- Hierarchical Clustering (DistMatrix)< 1 %
- Decision Tree Learner< 1 %
- K Nearest Neighbor< 1 %
- MDS< 1 %
PCA Compute< 1 %PCA< 1 %Logistic Regression Learner< 1 %- Logistic Regression Predictor< 1 %
Regression Predictor< 1 %- SVM Predictor< 1 %
- PCA Apply< 1 %
Gradient Boosted Trees Learner (Regression)< 1 %Tree Ensemble Learner< 1 %- Tree Ensemble Learner< 1 %
Tree Ensemble Predictor< 1 %Random Forest Learner< 1 %- Random Forest Learner (Regression)< 1 %
- Random Forest Predictor< 1 %
- Add Empty Rows< 1 %
- Binner (Dictionary)< 1 %
- Cell Splitter< 1 %
- Column Combiner< 1 %
- Category to Number< 1 %
- Number to Category (Apply)< 1 %
Number To String< 1 %- String to Number< 1 %
- Lag Column< 1 %
- Constant Value Column< 1 %
- Linear Correlation< 1 %
- Correlation Filter< 1 %
- Domain Calculator< 1 %
- Equal Size Sampling< 1 %
- Filter Apply< 1 %
- Nominal Value Row Filter< 1 %
- Nominal Value Row Splitter< 1 %
- Column Filter (PMML)< 1 %
- Many to One (PMML)< 1 %
Number To String (PMML)< 1 %String To Number (PMML)< 1 %- Sorter< 1 %
- Table Transposer< 1 %
- Ruleset Editor< 1 %
- Cronbach Alpha< 1 %
- Data Explorer< 1 %
- Kruskal-Wallis Test< 1 %
- Wilcoxon-Mann-Whitney Test< 1 %
- Statistics< 1 %
CASE Switch Data (End)< 1 %- Extract Table Dimension< 1 %
- Conditional Box Plot (legacy)< 1 %
- Enrichment Plotter (legacy)< 1 %
- HiLite Table (legacy)< 1 %
- Histogram (legacy)< 1 %
- Pie chart (legacy)< 1 %
- ROC Curve (legacy)< 1 %
- SDF Writer< 1 %
- DB Writer< 1 %
- Mahalanobis Distance< 1 %
- Numeric Distances< 1 %
- Matrix Distance< 1 %
- Box Plot (JavaScript)< 1 %
- Pie/Donut Chart (JavaScript)< 1 %
- Model Loop End< 1 %
PMML Ensemble Predictor< 1 %- PMML Ensemble to Table< 1 %
- Prediction Fusion< 1 %
- Cell to PMML< 1 %
- DL4J Feedforward Learner (Pretraining) (legacy)< 1 %
- Math Formula (Multi Column)< 1 %
- HeatMap (JFreeChart)< 1 %
- Histogram Chart (JFreeChart)< 1 %
- OSM Map to Image< 1 %
XLS Sheet Appender< 1 %Excel Writer (XLS)< 1 %TIBCO File Writer< 1 %Stanford tagger< 1 %- Bag Of Words Creator< 1 %
- Tag Cloud< 1 %
- Weka Cluster Assigner (3.7)< 1 %
- Nominal Row Filter Configuration< 1 %
- Heatmap (JavaScript)< 1 %
- ROC Curve (JavaScript)< 1 %
- Image Normalizer< 1 %
Python Script (1⇒1)< 1 %- Text Classifier Learner< 1 %
Views
- Statistics View
- Shows the WithinClusterVariation and the BetweenClusterVariation, which are indicators for 'good' clustering.
Workflows
ClusterKNIME Hub- Clustering_RetrievalKNIME Hub
- Clustering_RetrievalKNIME Hub
- Extract Fuzzy ClustersKNIME Hub
- Generic EChart_examplesKNIME Hub
- Show all 7 workflows
Links
- No links available
Developers
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