Core Partitioning: K-means and Similarity Clustering

K-means is arguably the most popular cluster-analysis method. The method’s output is twofold: (1) a partition of the entity set into clusters, and (2) centers representing the clusters. The method is rather intuitive and usually requires just a few pages to get presented. In contrast, this text includes a number of less popular subjects that are much important when using K-means for real-world data analysis: