Machine Learning with R, the tidyverse, and mlr

Chapter 16. Clustering by finding centers with k-means

This chapter covers

• Understanding the need for clustering
• Understanding over- and underfitting for clustering
• Validating the performance of a clustering algorithm

Our first stop in clustering brings us to a very commonly used technique: k-means clustering. I’ve used the word technique here rather than algorithm because k-means describes a particular approach to clustering that multiple algorithms follow. I’ll talk about these individual algorithms later in the chapter.

Note

Don’t confuse k-means with k-nearest neighbors! K-means is for unsupervised learning, whereas k-nearest neighbors is a supervised algorithm for classification.

K-means clustering attempts to learn ...