Building Machine Learning Systems with Python - Third Edition

K-means

K-means is the most widely used flat clustering algorithm. After initializing it with the desired number of clusters, num_clusters, it maintains that number of so-called cluster centroids. Initially, it will pick any num_clusters posts and set the centroids to their feature vector. Then it will go through all other posts and assign them the nearest centroid as their current cluster. Following this, it will move each centroid into the middle of all the vectors of that particular class. This changes, of course, the cluster assignment. Some posts are now nearer to another cluster. So it will update the assignments for those changed posts. This is done as long as the centroids move considerably. After some iterations, the movements will ...