## Introduction

*Hierarchical cluster analysis* is an algorithm that groups similar objects into groups called *clusters*. The endpoint is a set of clusters*, *where each cluster is distinct from each of the other clusters, and the objects within each cluster are broadly similar to each other. This article describes how to conduct a hierarchical cluster analysis in Displayr.

## Requirements

- Hierarchical clustering can be performed with either
*raw data*or a*distance matrix.*When raw data is used, the a distance matrix is automatically computed in the background.

## Method

1. From the toolbar menu, select **Anything > Advanced Analysis > Cluster > Hierarchical cluster analysis**.

2. From the object inspector on the right, select the variables from your data set that you want to use as inputs to the cluster analysis. For this example, we've used binary variables showing device ownership from a technology survey.

3. Enter a value for the **Number of clusters** that you want to create.

4. OPTIONAL: Select a distance measure from the **Distance** input. *Euclidean* is selected by default. For more information, see the dist package documentation which is used for the distance matrix computation.

5. OPTIONAL: Select the algorithm to use to form the clusters from the **Clustering method** input. The *Ward2* algorithm is selected by default. For more details, see the hclust package documentation.

6. Click the **Calculate** button to generate the custom analysis output.

The output is what's called a *dendrogram* which shows the distance between the variables. Each of the clusters is displayed as a separate color.

## See Also

How to Analyze Data by Groups/Segments

How to do Latent Class Analysis

How to Analyze Data by Groups/Segments

How to Create a Segmentation Comparison Table

How to do Mixed Mode Cluster Analysis in Displayr

How to do K-Means Cluster Analysis

How to Save K-Means Cluster Membership

## Comments

0 comments

Article is closed for comments.