# Hierarchical cluster analysis calculator

If you want to calculate a hierarchical cluster analysis online, just copy your data into the upper table and select one or more metric variables. Additionally you can select the column with the labels.

Hierarchical cluster analysis is used when you want to cluster data without knowing the number of clusters in advance. Using hierarchical cluster analysis you can then visualize the distance relationships between the data.

Different linking methods and distances can be used to calculate the hierarchical cluster analysis. The linkage methods and distances are available for selection:

**Linkage methods**: Single-linkage, Complete-linkage, Average-linkage,**Distances**: Euclidean, Manhattan, Maximum

## What is a Hierarchical cluster analysis?

Hierarchical cluster analysis (HCA) is a method of clustering that creates a hierarchical tree, or dendrogram, of the objects being clustered. This tree is a representation of the relationships between the objects, and it shows how the objects are grouped together into clusters at different levels of granularity.

Compared to the k-means cluster analysis, the number of clusters does not have to be specified in advance for the hierarchical cluster analysis. be specified in advance.

There are two main types of hierarchical clustering: agglomerative and divisive. In agglomerative clustering, the objects are first treated as individual clusters and then merged together into larger clusters as the analysis progresses. In divisive clustering, the objects are first treated as a single cluster and then split into smaller clusters as the analysis progresses.

## How is a Hierarchical Cluster Analysis calculated?

The process of creating the dendrogram starts by computing a distance matrix between all pairs of objects. This distance matrix is then used to create a linkage matrix, which contains information about the distance between clusters at each stage of the analysis. The linkage matrix is then used to create the dendrogram, which shows how the clusters are related to each other.

## When is a Hierarchical Cluster Analysis used?

HCA is useful in many applications such as biology, marketing, and social science. In biology, it can be used to identify patterns in genetic data. In marketing, it can be used to segment customers into different groups. In social science, it can be used to identify patterns in survey data.