Clustering in Tableau - Cell-by-Cell Formula Trace

Sample Data

Sample customer data with Annual Income and Spending Score used for clustering analysis.

Cell	Value
A1	CustomerID
B1	Annual Income
C1	Spending Score
A2	C001
B2	45000
C2	39
A3	C002
B3	54000
C3	81
A4	C003
B4	32000
C4	6
A5	C004
B5	76000
C5	77
A6	C005
B6	58000
C6	40

Formula Trace

CLUSTER([Annual Income], [Spending Score])

Step 1: Identify features: Annual Income and Spending Score for each customer

Step 2: Apply clustering algorithm (e.g., k-means) to group customers based on feature similarity

Step 3: Assign cluster labels to each customer

Cell Reference Map

    A           B               C
1 CustomerID  Annual Income  Spending Score
2   C001       45000           39
3   C002       54000           81
4   C003       32000            6
5   C004       76000           77
6   C005       58000           40

Features used: Columns B and C for clustering

The clustering formula uses Annual Income (B) and Spending Score (C) columns as input features.

Result

    A           B               C               D
1 CustomerID  Annual Income  Spending Score  Cluster
2   C001       45000           39             2
3   C002       54000           81             1
4   C003       32000            6             3
5   C004       76000           77             1
6   C005       58000           40             2

The final output adds a Cluster column showing the cluster number assigned to each customer.

Sheet Trace Quiz - 3 Questions

Which columns are used as features for clustering in this example?

A Annual Income and Spending Score

B CustomerID and Spending Score

C CustomerID and Annual Income

D Only Spending Score

Key Result

CLUSTER groups data points based on similarity of selected numeric features.

Practice

1.

What is the main purpose of clustering in Tableau?

easy

A. To create bar charts

B. To sort data alphabetically

C. To group similar data points automatically

D. To filter data by date

Solution

1. Step 1: Understand clustering concept

   Clustering groups data points that share similar characteristics without manual labeling.

2. Step 2: Identify Tableau's clustering use

   Tableau uses clustering to find natural groups in data automatically.

3. Final Answer:

   To group similar data points automatically -> Option C

4. Quick Check:

   Clustering = Group similar data [OK]

Hint: Clustering means grouping similar items automatically [OK]

Common Mistakes:

• Confusing clustering with sorting
• Thinking clustering creates charts
• Assuming clustering filters data

2.

Which step correctly adds clusters in Tableau?

1. Drag fields to Rows and Columns
2. Open Analytics pane
3. Drag 'Cluster' onto the view
4. Adjust cluster count

easy

A. Use the 'Show Me' panel to add clusters

B. Drag 'Cluster' from Data pane to view

C. Right-click view and select 'Add Cluster'

D. Drag 'Cluster' from Analytics pane to view

Solution

1. Step 1: Identify where 'Cluster' is located

   In Tableau, 'Cluster' is found in the Analytics pane, not Data pane or menus.

2. Step 2: Confirm correct action to add clusters

   You drag 'Cluster' from Analytics pane onto the view to create clusters.

3. Final Answer:

   Drag 'Cluster' from Analytics pane to view -> Option D

4. Quick Check:

   Clusters come from Analytics pane [OK]

Hint: Clusters come from Analytics pane, not Data pane [OK]

Common Mistakes:

• Trying to drag 'Cluster' from Data pane
• Looking for 'Add Cluster' in right-click menu
• Using 'Show Me' panel for clusters

3.

Given this Tableau clustering setup, what is the expected result?

- Data: Customers with sales and profit
- Clusters: 3 groups based on sales and profit
- View: Scatter plot of Sales vs Profit with clusters colored

What will the clusters show?

medium

A. Three groups of customers with similar sales and profit patterns

B. Three groups sorted by customer name alphabetically

C. Three groups based on sales only, ignoring profit

D. Random groups unrelated to sales or profit

Solution

1. Step 1: Understand clustering variables

   Clusters use sales and profit to group customers with similar values in both.

2. Step 2: Interpret cluster meaning on scatter plot

   Each cluster groups customers close in sales and profit, shown by colors.

3. Final Answer:

   Three groups of customers with similar sales and profit patterns -> Option A

4. Quick Check:

   Clusters group by sales and profit [OK]

Hint: Clusters group by all selected fields, not just one [OK]

Common Mistakes:

• Assuming clusters sort alphabetically
• Thinking clusters use only one variable
• Believing clusters are random

4.

What is wrong with this clustering setup in Tableau?

- Added 'Cluster' from Analytics pane
- Selected only one field: 'Customer ID'
- Result: All data points in one cluster

medium

A. Cluster count must be set to zero to work

B. Using only one unique ID field prevents meaningful clusters

C. Clusters require at least three fields to function

D. Cluster feature is not available for Customer ID field

Solution

1. Step 1: Analyze field choice for clustering

   Using a unique identifier like Customer ID does not group similar data; each ID is unique.

2. Step 2: Understand cluster result

   Since all IDs are unique, Tableau places all points in one cluster by default.

3. Final Answer:

   Using only one unique ID field prevents meaningful clusters -> Option B

4. Quick Check:

   Unique IDs don't create clusters [OK]

Hint: Avoid unique ID fields alone for clustering [OK]

Common Mistakes:

• Setting cluster count to zero
• Thinking clusters need 3+ fields
• Believing Customer ID can't be used at all

5.

You want to segment customers into 4 clusters using sales, profit, and discount in Tableau. Which approach is best?

1. Create a scatter plot with sales and profit
2. Add discount as a cluster variable
3. Drag 'Cluster' from Analytics pane to view
4. Set number of clusters to 4

What should you do to ensure meaningful clusters?

hard

A. Include all three fields (sales, profit, discount) in clustering and set clusters to 4

B. Use only sales and profit, ignore discount, and set clusters to 4

C. Set clusters to 2 for better separation with three fields

D. Create separate clusters for each field individually

Solution

1. Step 1: Include all relevant fields for clustering

   Using sales, profit, and discount together helps find groups based on all factors.

2. Step 2: Set cluster count as desired

   Setting clusters to 4 matches the goal of segmenting into four groups.

3. Final Answer:

   Include all three fields (sales, profit, discount) in clustering and set clusters to 4 -> Option A

4. Quick Check:

   Use all fields + 4 clusters [OK]

Hint: Use all relevant fields and set cluster count as needed [OK]

Common Mistakes:

• Ignoring discount field
• Choosing wrong cluster count
• Creating separate clusters per field