Clustering data with the k-means method

K-means clustering is a method of partitioning clustering. The goal of the algorithm is to partition n objects into k clusters, in which each object belongs to the cluster with the nearest mean. Unlike hierarchical clustering, which does not require a user to determine the number of clusters at the beginning, the k-means method does require 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 hotel location dataset.

Getting ready

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

How to do it…

Please perform the following steps to cluster the hotel location dataset with the k-means method:

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

   > set.seed(22)
   > fit <- kmeans(hotel[,c(&quot...