Clustering data with hierarchical clustering

Hierarchical clustering adopts either an agglomerative or divisive method to build a hierarchy of clusters. Regardless of which approach is adopted, both first use a distance similarity measure to combine or split clusters. The recursive process continues until there is only one cluster left or you cannot split more clusters. Eventually, we can use a dendrogram to represent the hierarchy of clusters. In this recipe, we will demonstrate how to cluster customers with hierarchical clustering.

Getting ready

In this recipe, we will perform hierarchical clustering on customer data, which involves segmenting customers into different groups. You can download the data from this GitHub page: https://github.com/ywchiu/ml_R_cookbook/tree/master/CH9.

How to do it...

Perform the following steps to cluster customer data into a hierarchy of clusters:

1. First, you need to load data from customer.csv and save it into customer:

    > customer= read.csv('customer.csv...