Fields Not Supported for Clustering in Tableau

In today’s data-driven world, simply visualizing data is no longer enough. Business leaders want answers to deeper questions:
Which customers behave similarly?
Which products belong to the same performance group?
Which regions show comparable growth patterns?
This is where clustering becomes a powerful analytical technique. Tableau makes advanced clustering accessible—even for users without a strong statistical background—by embedding it directly into its visual analytics workflow.
In this article, we will explore:
What clustering is and why it matters
How Tableau implements clustering
Step-by-step clustering using Tableau
How to interpret cluster quality and statistics
Practical business use cases
Limitations and best practices
Let’s dive in.

What Is Clustering?
Clustering is an unsupervised machine learning technique used to group similar observations or data points based on shared characteristics. Unlike classification, clustering does not rely on predefined labels. Instead, it discovers natural groupings hidden within the data.
A Simple Business Example
Consider a car manufacturer analyzing customer preferences:
Cluster 1: Customers looking for small cars under $6,000
Cluster 2: Customers interested in mid-range sedans
Cluster 3: Customers willing to spend $30,000+ on premium SUVs
By identifying these clusters, the manufacturer can:
Optimize product design
Tailor marketing campaigns
Improve pricing strategies
Forecast demand more accurately
Clustering helps businesses move from intuition-based decisions to data-backed segmentation.

How Tableau Performs Clustering
Tableau uses the K-means clustering algorithm, one of the most widely used clustering methods.
How K-means Works (Conceptually)
You specify the number of clusters (k)
Tableau places k centroids (center points)
Each data point is assigned to the nearest centroid
Centroids are recalculated based on assigned points
Steps 3 and 4 repeat until clusters stabilize
The objective is to minimize the total distance between data points and their respective cluster centroids.
The beauty of Tableau is that all of this happens behind the scenes—allowing analysts to focus on interpretation rather than computation.

Getting Started: Preparing the Dataset
To demonstrate clustering, download the sample dataset provided in the link you referenced (for example, a flower dataset containing petal length, petal width, and species).
Once downloaded:
Open Tableau Desktop
Load the dataset
Review the available dimensions and measures
The dataset contains measurements of flowers across three species, making it ideal for clustering demonstrations.

Creating the Initial Visualization
Let’s begin by visualizing the relationship between two measures:
Petal Length
Petal Width
Steps:
Drag Petal Length to Columns
Drag Petal Width to Rows
By default, Tableau aggregates measures, resulting in a single point. To see individual observations:
Go to Analysis in the top menu
Uncheck Aggregate Measures
You should now see a scatter plot representing individual data points.
This visual foundation is critical—clustering in Tableau always starts from an existing visualization.

Applying Clustering in Tableau
Now comes the fun part.
Steps to Add Clusters:
Open the Analytics pane
Drag Cluster onto the scatter plot
Drop it anywhere in the view
Tableau automatically:
Chooses an initial number of clusters
Uses the measures present in the view
Assigns each point to a cluster
At this stage, Tableau gives you a working model—but not necessarily the best one.

Customizing Clusters
Tableau allows full control over clustering behavior.
Adjusting the Number of Clusters
Click the Clusters pill
Increase or decrease the number of clusters
Observe how cluster boundaries change
Selecting Variables for Clustering
You can explicitly define which measures Tableau should use:
Drag additional measures into the clustering configuration
Remove irrelevant measures to avoid noise
This flexibility allows you to align clusters with specific business questions.

Understanding Cluster Quality: Model Description
Clustering is only useful if it’s statistically meaningful.
Tableau provides transparency through the “Describe Clusters” option:
Click the cluster pill
Select Describe Clusters
This opens a detailed summary including:
Variables used
Cluster centers
Statistical significance metrics
Let’s break down the two most important metrics.

Key Statistical Metrics Explained
F-Statistic (F-Ratio)
The F-statistic measures how well a variable distinguishes between clusters.
F=Between-Group VariabilityWithin-Group VariabilityF = \frac{\text{Between-Group Variability}}{\text{Within-Group Variability}}F=Within-Group VariabilityBetween-Group Variability​
Higher F-values indicate stronger differentiation
Variables with low F-values contribute little to cluster separation
P-Value
The p-value measures statistical significance:
A low p-value means the variable significantly differentiates clusters
Typically, p-values below 0.05 are considered meaningful
Together, F-statistic and p-value help validate whether clusters are meaningful—or just visually appealing noise.

Saving Clusters for Further Analysis
Clusters don’t have to remain temporary.
You can:
Drag the Cluster field from the Marks card
Drop it into Dimensions
This converts clusters into a reusable group that can be:
Filtered
Used in dashboards
Combined with other dimensions
Analyzed across different views

Fields Not Supported for Clustering in Tableau
Tableau restricts certain fields from clustering to preserve statistical integrity:
Dates
Bins
Sets
Table Calculations
Blended Calculations
Ad-hoc Calculations
Parameters
Generated Latitude/Longitude
Understanding these limitations helps avoid confusion when configuring clusters.

A Second Example: Clustering Countries Using World Indicators
Tableau’s World Indicators sample dataset is perfect for real-world clustering.
Example Scenario:
Cluster countries based on:
Life expectancy
Population over age 65
Urban population percentage
Steps:
Open World Indicators sample workbook
Create a map or scatter plot using these measures
Apply clustering from the Analytics pane
Insights You Can Derive:
Identify aging economies
Spot developing vs developed regions
Compare healthcare and urbanization trends
You can also:
Select a cluster
Switch to a text table
View all countries belonging to that cluster
This makes clustering highly actionable for policy analysis, market entry decisions, and global strategy.

Business Use Cases of Clustering in Tableau
Clustering can be applied across industries:
Marketing: Customer segmentation and personalization
Finance: Risk profiling and portfolio grouping
Healthcare: Patient stratification
Retail: Product and store performance analysis
Operations: Supplier and logistics optimization
The key is not just creating clusters—but interpreting and acting on them.

Best Practices for Effective Clustering
Start with clear business questions
Normalize data when scales vary widely
Avoid too many variables at once
Validate clusters using statistical metrics
Always interpret clusters in business context
Clustering is exploratory by nature—iteration is expected.

Conclusion
Clustering in Tableau bridges the gap between advanced analytics and business usability. With just a few drag-and-drop actions, you can uncover hidden structures, segment data meaningfully, and generate insights that directly support decision-making.
While this article covered only a few scenarios, the true power of clustering lies in experimentation and interpretation. Try different datasets, vary measures, and question the patterns you observe.
Keep exploring.
Keep practicing.
And most importantly—let your data tell its story.
Happy Clustering!
At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. Our services include delivering end-to-end tableau consulting services and working as a trusted Power BI Consulting Company, turning data into strategic insight. We would love to talk to you. Do reach out to us.