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 ...
Get R: Recipes for Analysis, Visualization and Machine Learning now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
