Icon

Customer Segmentation

KNIME Workflow for KMeans Clustering and VisualizationOverviewThis workflow demonstrates the process of performing KMeans clustering on a dataset, assigning descriptive labels to the clusters, and visualizing the results using scatter plots.The workflow includes data preprocessing steps, clustering, and post-processing to label and visualize the clusters. **Nodes and Configuration****1. File Reader**- **Function**: Reads the input dataset from a CSV file.- **Configuration**: Load your data from a CSV file. Make sure the file path is correctly specified.**2. K-Means**- **Function**: Performs KMeans clustering on the dataset.- **Configuration**: - **Number of clusters**: Specify the desired number of clusters. - **Features to use for clustering**: Select the features that will be used for clustering. **3. Rule Engine**- **Function**: Applies specific rules to the clustered data to assign descriptive labels to each cluster.- **4. Color Manager**- **Function**: Manages the colors of the data points for better visualization.- **Configuration**: Assign different colors to each cluster based on the descriptive labels. **5. Shape Manager **- **Function**: Manages the shapes of the data points for better visualization.- **Configuration**: Assign different shapes to each cluster based on the descriptive labels. **6. Scatter Plot (legacy)**- **Function**: Visualizes the clustered data using a scatter plot.- **Configuration**: Select the appropriate axes and options for visualizing the clusters.**7. Scatter Plot (JavaScript)**- **Function**: Visualizes the clustered data using an interactive scatter plot.- **Configuration**: Select the appropriate axes and options for visualizing the clusters interactively.**Workflow Steps**1. **Data Loading**: The workflow starts by reading the input dataset using the **File Reader** node.2. **KMeans Clustering**: The **K-Means** node performs clustering on the dataset based on the selected features.3. **Assign Descriptive Labels**: The **Rule Engine** node assigns descriptive labels to each cluster based on specific rules.4. **Color and Shape Management**: The **Color Manager** and **Shape Manager** nodes manage the colors and shapes of the data points for better visualization.5. **Visualization**: The clustered data is visualized using the **Scatter Plot (legacy)** and **Scatter Plot (JavaScript)** nodes. **Conclusion**This workflow provides a comprehensive approach to clustering and visualizing data using KNIME. By following the steps outlined above, you can effectively perform KMeansclustering, assign descriptive labels to the clusters, and visualize the results.---Feel free to upload this documentation to KNIME Hub and share your workflow with the community! If you need any further assistance or modifications, just let me know. cut_segmentNode 2Node 4Node 7Node 8Node 9Node 12 CSV Reader k-Means Color Manager Shape Manager Scatter Plot(legacy) Scatter Plot(JavaScript) Rule Engine KNIME Workflow for KMeans Clustering and VisualizationOverviewThis workflow demonstrates the process of performing KMeans clustering on a dataset, assigning descriptive labels to the clusters, and visualizing the results using scatter plots.The workflow includes data preprocessing steps, clustering, and post-processing to label and visualize the clusters. **Nodes and Configuration****1. File Reader**- **Function**: Reads the input dataset from a CSV file.- **Configuration**: Load your data from a CSV file. Make sure the file path is correctly specified.**2. K-Means**- **Function**: Performs KMeans clustering on the dataset.- **Configuration**: - **Number of clusters**: Specify the desired number of clusters. - **Features to use for clustering**: Select the features that will be used for clustering. **3. Rule Engine**- **Function**: Applies specific rules to the clustered data to assign descriptive labels to each cluster.- **4. Color Manager**- **Function**: Manages the colors of the data points for better visualization.- **Configuration**: Assign different colors to each cluster based on the descriptive labels. **5. Shape Manager **- **Function**: Manages the shapes of the data points for better visualization.- **Configuration**: Assign different shapes to each cluster based on the descriptive labels. **6. Scatter Plot (legacy)**- **Function**: Visualizes the clustered data using a scatter plot.- **Configuration**: Select the appropriate axes and options for visualizing the clusters.**7. Scatter Plot (JavaScript)**- **Function**: Visualizes the clustered data using an interactive scatter plot.- **Configuration**: Select the appropriate axes and options for visualizing the clusters interactively.**Workflow Steps**1. **Data Loading**: The workflow starts by reading the input dataset using the **File Reader** node.2. **KMeans Clustering**: The **K-Means** node performs clustering on the dataset based on the selected features.3. **Assign Descriptive Labels**: The **Rule Engine** node assigns descriptive labels to each cluster based on specific rules.4. **Color and Shape Management**: The **Color Manager** and **Shape Manager** nodes manage the colors and shapes of the data points for better visualization.5. **Visualization**: The clustered data is visualized using the **Scatter Plot (legacy)** and **Scatter Plot (JavaScript)** nodes. **Conclusion**This workflow provides a comprehensive approach to clustering and visualizing data using KNIME. By following the steps outlined above, you can effectively perform KMeansclustering, assign descriptive labels to the clusters, and visualize the results.---Feel free to upload this documentation to KNIME Hub and share your workflow with the community! If you need any further assistance or modifications, just let me know. cut_segmentNode 2Node 4Node 7Node 8Node 9Node 12CSV Reader k-Means Color Manager Shape Manager Scatter Plot(legacy) Scatter Plot(JavaScript) Rule Engine

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: saxen
Created at: 2024-04-02
On NodePit since: 2025-01-08
Last update: 2025-02-22
Created with KNIME version: v5.2.5

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!