How Can You Perform Cluster Analysis in Excel?

Cluster analysis is a method of grouping similar data points. It enables the finding of patterns and similarities in a dataset. In this article, we are going to guide you on how to perform cluster analysis in Excel. Even as a beginner, you can follow these steps quite easily!

What is cluster analysis?

Cluster analysis is a method of grouping data into clusters or groups based on their similarity. For example, if you have data about animals, you might want to group them into clusters like mammals, birds, and reptiles based on similar features.

Steps to Perform Cluster Analysis in Excel

You can carry out cluster analysis in Excel, but you would need to have the Excel add-ins or some external tools. Here are the steps for running a cluster analysis in Excel.

Prepare Your Data

The first thing that you have to do is prepare your data. Say you have data about various items and their features. Something like this:

Item Feature 1 Feature 2 Feature 3

A 10 15 20

B 11 14 22

C 50 55 60

D 51 58 63

Ensure that your data is in a table format, meaning each column should represent a feature, and each row an item.

2. Activating the Data Analysis Toolpak

You need to activate the Data Analysis Toolpak in Excel to carry out some advanced statistical operations in Excel, such as clustering.

From the File menu, click Options.
From here, click Add-ins.
In the Manage box of the dialog, click Excel Add-ins, and then click Go.
Check Analysis ToolPak under the list and click OK.

3. Use the K-means clustering method.

Although Excel does not have a direct “cluster analysis” tool, you can use the K-means clustering method, which happens to be one of the most-used clustering techniques. K-means will allow you to group similar data points.
K-means is a method where you specify how many groups (clusters) you want the algorithm to place the data into. The algorithm will place the data into what it deems the best clusters.
The best way to perform K-means clustering in Excel is to write VBA code or use third-party add-ins like XLSTAT or XLMiner. Here, we shall look at the steps in general terms without delving into the coding.

4. Using K-Means Cluster Analysis (Using Excel Add-ins like XLSTAT)

If you have XLSTAT installed, then here are the steps to follow:

Go to the XLSTAT tab in Excel.
Click on K-means clustering from the menu.
A dialog box will appear and prompt you to choose your data.
Choose how many clusters (groups) you want Excel to create.
Click OK. The program will now perform the clustering automatically and display the results in a table and/or chart.

5. Analyze Your Results

Once you have run the cluster analysis, Excel groups your data into clusters according to their feature similarity. You will now get a new column or table showing the clusters. It might look something like this:

Item Cluster

A 1

B 1

C 2

D 2

This means that items A and B form one cluster, and items C and D form another.

6. Visualize the clusters.

To understand the clusters better, you can plot them using a scatter plot.

Select your data
Go to the Insert tab.
Click on Scatter Chart
Excel will plot the points on a graph.
You can color the points based on their clusters for easier interpretation

Why is cluster analysis useful?

Cluster analysis is useful since it enables you to:

Group similar items together to aid decision-making.
Identify patterns in the data.
Group data points and make a large set of data sensible by forming them into smaller groups.

Conclusion

The best and most efficient way to group similar data points in Excel is to do cluster analysis. You could have achieved this by preparing your data, using K-means clustering with an external tool, XLSTAT, and then reviewing your results. Though Excel does not support direct clustering, with a little extra help, you can easily make cluster analysis possible.