Clustering data with the k-means method

k-means clustering is a flat clustering technique, which produces only one partition with k clusters. Unlike hierarchical clustering, which does not require a user to determine the number of clusters at the beginning, the k-means method requires this to be determined first. However, k-means clustering is much faster than hierarchical clustering as the construction of a hierarchical tree is very time consuming. In this recipe, we will demonstrate how to perform k-means clustering on the customer dataset.

Getting ready

In this recipe, we will continue to use the customer dataset as the input data source to perform k-means clustering.

How to do it...

Perform the following steps to cluster the customer dataset with the k-means method:

1. First, you can use kmeans to cluster the customer data:

    > set.seed(22)
    > fit = kmeans(customer, 4)
    > fit
    Output
    K-means clustering with 4 clusters of sizes 8, 11, 16, 25
    
    Cluster means:
   ...