15.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering
In this section, we introduce perhaps the simplest unsupervised machine learning algorithms—k-means clustering. This algorithm analyzes unlabeled samples and attempts to place them in clusters that appear to be related. The k in “k-means” represents the number of clusters you’d like to see imposed on your data.
The algorithm organizes samples into the number of clusters you specify in advance, using distance calculations similar to the k-nearest neighbors clustering algorithm. Each cluster of samples is grouped around a centroid—the cluster’s center point. Initially, the algorithm chooses k centroids at random from the dataset’s samples. Then the remaining samples are ...
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