Clustering in Tableau

Clustering in Tableau is a powerful feature that allows users to identify patterns or groups in their data that may not be immediately apparent. This unsupervised machine learning technique can automatically group similar data points together based on selected measures. Clustering can reveal insights such as customer segments, product groupings, or any other natural groupings in your data.

Step 1: Prepare Your Data

Ensure your data is clean and properly formatted for analysis. Tableau’s clustering feature works with numerical and boolean fields. If you have categorical fields you wish to include in your clustering, you might need to transform them into a format that can be used for the analysis.

Step 2: Create a Scatter Plot

Clustering often starts with visual exploration. A scatter plot can help you visually inspect the distribution of your data points across two dimensions.

1. Drag and drop one measure to the Columns shelf and another to the Rows shelf.
2. From the data pane, drag the dimension or dimensions that you want to analyze to the Detail shelf in the Marks card.

Step 3: Apply Clustering

1. With your scatter plot created, go to the Analytics pane, which is next to the Data pane.
2. Under the Model section, you’ll find “Cluster.” Drag this option into your view, and Tableau will automatically create clusters based on the data points in your scatter plot.
3. Tableau will present you with a suggested number of clusters, but you can adjust this number by editing the cluster options. You can access these options by clicking on the cluster in the Marks card and selecting “Edit.”

Step 4: Analyze the Results

Once clusters have been created, Tableau will color-code them in your view. You can further analyze these clusters by:

• Examining the cluster centroids in the summary provided by Tableau, which shows the average values of each measure for each cluster.
• Dragging additional dimensions or measures to the Tooltip shelf in the Marks card to get more detailed information when you hover over a data point.
• Using the Describe Clusters feature (by right-clicking the cluster in the Marks card) to see a statistical summary and the distinguishing characteristics of each cluster.

Step 5: Refine Your Clusters

You might need to refine your clusters for better insights:

• Experiment with including or excluding different measures or dimensions from your analysis to see how it impacts the clustering.
• Adjust the number of clusters to see if a different configuration provides more meaningful insights.

Step 6: Share Your Findings

Once you’ve identified meaningful clusters, you can share your findings by creating a dashboard. Incorporate other related visualizations to tell a compelling story about your data.

Tips for Effective Clustering in Tableau

• Data Preprocessing:

Consider normalizing your data, especially if the measures have different scales.

• Interpretability:

Choose a number of clusters that make sense for your application. More clusters might provide finer distinctions but can be harder to explain.

• Cluster Validation:

Validate your clusters by checking if they make practical sense. You might need domain knowledge to interpret clusters effectively.