The major weakness of k-means clustering is that it only works well with numeric data because a distance metric must be computed. There are a few advanced clustering techniques that can deal with ...

K-means clustering, inertia & silhouette score K-means clustering is very effective when segmenting data and attempting to find patterns. Because clustering is used in a broad array of industries, ...

This package includes python functions that implement k-means clustering from scratch. This will work on any dataset with valid numerical features, and includes fit, predict, and cluster_summary ...