Implementing K-Means and Hierarchical Clustering in Pure C for Customer Segmentation

A developer guide demonstrates how to build K-Means and single-linkage hierarchical clustering from scratch in pure C, rather than relying on Python libraries like scikit-learn. The tutorial applies both methods to a retail customer segmentation problem using purchase frequency, spending score, and age. It determines the optimal cluster count with the Elbow Method, which computes within-cluster sum of squares (WCSS) and points to two segments. The article uses a small six-customer dataset so readers can verify Euclidean distance calculations by hand. It also includes an ASCII terminal visualizer and a streamlined flat-array version of the code for prototyping and coursework.