Clustering is a powerful analytical technique used to group similar data points together based on their characteristics. In Tableau, clustering can help you identify patterns and segments within your data, which can be crucial for making informed business decisions.

1. Clusters: Groups of data points that are similar to each other.
2. Centroids: The center point of a cluster.
3. K-means Algorithm: A popular clustering algorithm used in Tableau.
4. Distance Metrics: Measures used to determine the similarity between data points.

Let's walk through a practical example of how to create clusters in Tableau.

Step-by-Step Guide

1. Load Your Data:

   • Open Tableau and connect to your data source. For this example, we'll use a sample dataset containing sales data.

2. Create a Scatter Plot:

   • Drag Sales to the Columns shelf.
   • Drag Profit to the Rows shelf.
   • Drag Category to the Color shelf to differentiate between different product categories.

3. Add Clusters:

   • Click on the Analytics pane.
   • Drag the Cluster option onto the scatter plot.
   • Tableau will automatically create clusters based on the K-means algorithm.

4. Adjust the Number of Clusters:

   • By default, Tableau will choose the number of clusters. You can adjust this by clicking on the Clusters card and changing the number of clusters.

5. Analyze the Clusters:

   • Observe the clusters created by Tableau. Each cluster will be color-coded, and you can see the centroids of each cluster.

Code Example

Here's a simple example of how you might set up clustering in Tableau using a sample dataset:

Exercise: Create clusters using the Sample - Superstore dataset to identify different customer segments based on Sales and Profit.

1. Connect to the Sample - Superstore dataset.
2. Create a scatter plot with Sales on the Columns shelf and Profit on the Rows shelf.
3. Add Category to the Color shelf.
4. Use the Analytics pane to add clusters.
5. Adjust the number of clusters to 4.
6. Analyze the resulting clusters and describe the characteristics of each cluster.

Solution:

1. Connect to the Sample - Superstore dataset.
2. Drag Sales to Columns.
3. Drag Profit to Rows.
4. Drag Category to Color.
5. Go to the Analytics pane and drag Cluster to the view.
6. Set the number of clusters to 4.
7. Analyze the clusters:
   • Cluster 1: High sales, high profit.
   • Cluster 2: Low sales, low profit.
   • Cluster 3: High sales, low profit.
   • Cluster 4: Low sales, high profit.

• Choosing the Number of Clusters: Selecting the right number of clusters is crucial. Too few clusters may oversimplify the data, while too many clusters may overcomplicate it.
• Interpreting Clusters: Always interpret the clusters in the context of your business problem. Clusters should provide actionable insights.
• Data Preparation: Ensure your data is clean and preprocessed before applying clustering. Outliers and missing values can significantly affect the results.

Clustering is a valuable technique in Tableau for segmenting data and uncovering hidden patterns. By following the steps outlined in this section, you can effectively create and analyze clusters to gain deeper insights into your data. In the next section, we will explore another advanced analytical technique: Reference Lines and Bands.